Edited, memorised or added to reading list

on 19-Mar-2017 (Sun)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

The RNG Key allows the pilot to select the desired map range. Use the up arrow of the key to zoom out to a larger area, or the down arrow to zoom in to a smaller area.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#biochem #biology #cell
At high enough concentrations in a test tube, FUS protein forms a hydrogel that will associate with either itself or with the low complexity domains from other proteins.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#deeplearning #neuralnetworks
Kullbac k-Leibler (KL) div ergence : \(D_{KL}( P||Q) = E_{x\sim P} = [log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \) (3.50) In the case of discrete v ariables, it is the extra amount of information (measured in bits if we use the base 2 logarithm, (but in machine learning w e usually use nats and the natural logarithm) needed to send a message containing symbols drawn from probability distribution P , when w e use a co de that w as designed to minimize the length of messages dra wn from probabilit y distribution Q.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#deeplearning #neuralnetworks
A quan tit y that is closely related to the KL div ergence is the cross-en trop y \(H ( P , Q ) = H ( P ) + D_{KL}( P|| Q )\) , whic h is similar to the KL div ergence but lac king the term on the left: \(H (P, Q ) = − E _{x \sim P} \space log Q(x)\)

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#deeplearning #neuralnetworks
Both of these difficulties can b e resolved by instead ev aluating softmax ( z ) where z = x − maxi xi . Simple algebra shows that the v alue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtracting max i x i results in the largest argument to exp b eing 0, whic h rules out the p ossibility of ov erflo w. Lik ewise, at least one term in the denominator has a v alue of 1, which rules out the p ossibility of underflow in the denominator leading to a division by zero

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#deeplearning #neuralnetworks
W e usually phrase most optimization problems in terms of minimizing f ( x ) . Maximization ma y b e accomplished via a minimization algorithm by minimizing − f (x)

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1489898704140

Question
what is the name of your mother
Answer
ChaCha


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill






#ja #johtamis #markkinointiajattelunkehittyminen
Klassisissa teorioissa organisaatioiden toimintaa tarkasteltiin organisaatioiden sisäisten toimintojen tehokkuuden kannalta. Johdon tehtävänä nähtiin olevan organisaation sisäisen tehokkuuden rationaalinen optimointi. Tämä uskottiin saatavan aikaan luomalla niin yksinkertainen organisaatiorakenne ja tehtävänjako, että tehtäviin liittyvät päämäärät voitaisiin tavoittaa. Ihmisiä pidettiin välineinä johdon asettamien tavoitteiden saavuttamisessa. Organisaatiot siis miellettiin koneen kaltaisiksi ja ihmiset siellä koneen vaihtokelpoisiksi osiksi (ks. Morgan 1998). Ajan vallitseva ihmiskuva oli hyvin mekanistinen.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1490894064908

Tags
#matlab #programming
Question
[...],whereh is an integer, creates a new figure window, or makes figure h the current figure
Answer
figure(h)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
figure(h),whereh is an integer, creates a new figure window, or makes figure h the current figure

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490895637772

Tags
#matlab #programming
Question
figure(h),whereh is an [...], creates a new figure window, or makes figure h the current figure
Answer
integer


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
figure(h),whereh is an integer, creates a new figure window, or makes figure h the current figure

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490897210636

Tags
#matlab #programming
Question
figure(h),whereh is an integer, [...]
Answer
creates a new figure window, or makes figure h the current figure


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
figure(h),whereh is an integer, creates a new figure window, or makes figure h the current figure

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490898783500

Tags
#matlab #programming
Question
[...] deletes all plots and text from the current axes, i.e., leaves only the x- and y-axes and their associated information
Answer
cla


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
cla deletes all plots and text from the current axes, i.e., leaves only the x- and y-axes and their associated information

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490900356364

Tags
#matlab #programming
Question
cla [...]
Answer
deletes all plots and text from the current axes, i.e., leaves only the x- and y-axes and their associated information


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
cla deletes all plots and text from the current axes, i.e., leaves only the x- and y-axes and their associated information

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490901929228

Tags
#matlab #programming
Question
The function [...] can take a second input variable. It can be a scalar spec- ifying how many contour levels to plot, or it can be a vector specifying the values at which to plot the contour levels
Answer
contour


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The function contour can take a second input variable. It can be a scalar spec- ifying how many contour levels to plot, or it can be a vector specifying the values at which to plot the contour levels

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490903502092

Tags
#matlab #programming
Question
The function contour can take a second input variable. It can be a [...], or it can be a vector specifying the values at which to plot the contour levels
Answer
scalar spec- ifying how many contour levels to plot


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The function contour can take a second input variable. It can be a scalar spec- ifying how many contour levels to plot, or it can be a vector specifying the values at which to plot the contour levels

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490905074956

Tags
#matlab #programming
Question
The function contour can take a second input variable. It can be a scalar spec- ifying how many contour levels to plot, or it can be a [...]
Answer
vector specifying the values at which to plot the contour levels


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The function contour can take a second input variable. It can be a scalar spec- ifying how many contour levels to plot, or it can be a vector specifying the values at which to plot the contour levels

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490906647820

Tags
#matlab #programming
Question
The function [...] draws little arrows to indicate a gradient or other vec- tor field
Answer
quiver


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The function quiver draws little arrows to indicate a gradient or other vec- tor field

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490908220684

Tags
#matlab #programming
Question
The function quiver [...]
Answer
draws little arrows to indicate a gradient or other vec- tor field


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The function quiver draws little arrows to indicate a gradient or other vec- tor field

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490909793548

Tags
#matlab #programming
Question
The [...] function can also be used to ‘visualize’ a matrix
Answer
mesh


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The mesh function can also be used to ‘visualize’ a matrix

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490911366412

Tags
#matlab #programming
Question
The mesh function can also be used to [...]
Answer
‘visualize’ a matrix


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The mesh function can also be used to ‘visualize’ a matrix

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490912939276

Tags
#matlab #programming
Question
The function [...] takes two arguments. The first one, az in this example, is called the azimuth or polar angle in the x-y plane (in degrees). az rotates the viewpoint (you) about the z-axis—i.e. about the ‘pinnacle’ at (15,15) in Fig- ure 9.12—in a counter-clockwise direction. The default value of az is −37.5 ◦ . The program therefore rotates you in a counter-clockwise direction about the z-axis in 15 ◦ steps starting at the default position. The second argument of view is the vertical elevation el (in degrees). This is the angle a line from the viewpoint makes with the x-y plane. A value of 90 ◦ for el means you are directly overhead. Positive values of the elevation mean you are above the x-y plane; negative values mean you are below it. The default value of el is 30 ◦
Answer
view


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The function view takes two arguments. The first one, az in this example, is called the azimuth or polar angle in the x-y plane (in degrees). az rotates the viewpoint (you) about the z-axis—i.e. about th

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490914512140

Tags
#matlab #programming
Question
The function view takes two arguments. The first one, az in this example, is called the [...] in the x-y plane (in degrees). az rotates the viewpoint (you) about the z-axis—i.e. about the ‘pinnacle’ at (15,15) in Fig- ure 9.12—in a counter-clockwise direction. The default value of az is −37.5 ◦ . The program therefore rotates you in a counter-clockwise direction about the z-axis in 15 ◦ steps starting at the default position. The second argument of view is the vertical elevation el (in degrees). This is the angle a line from the viewpoint makes with the x-y plane. A value of 90 ◦ for el means you are directly overhead. Positive values of the elevation mean you are above the x-y plane; negative values mean you are below it. The default value of el is 30 ◦
Answer
azimuth or polar angle


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The function view takes two arguments. The first one, az in this example, is called the azimuth or polar angle in the x-y plane (in degrees). az rotates the viewpoint (you) about the z-axis—i.e. about the ‘pinnacle’ at (15,15) in Fig- ure 9.12—in a counter-clockwise direction. The default value

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490916085004

Tags
#matlab #programming
Question
The function view takes two arguments. The first one, az in this example, is called the azimuth or polar angle in the x-y plane (in degrees). az [...]—i.e. about the ‘pinnacle’ at (15,15) in Fig- ure 9.12—in a counter-clockwise direction. The default value of az is −37.5 ◦ . The program therefore rotates you in a counter-clockwise direction about the z-axis in 15 ◦ steps starting at the default position. The second argument of view is the vertical elevation el (in degrees). This is the angle a line from the viewpoint makes with the x-y plane. A value of 90 ◦ for el means you are directly overhead. Positive values of the elevation mean you are above the x-y plane; negative values mean you are below it. The default value of el is 30 ◦
Answer
rotates the viewpoint (you) about the z-axis


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The function view takes two arguments. The first one, az in this example, is called the azimuth or polar angle in the x-y plane (in degrees). az rotates the viewpoint (you) about the z-axis—i.e. about the ‘pinnacle’ at (15,15) in Fig- ure 9.12—in a counter-clockwise direction. The default value of az is −37.5 ◦ . The program therefore rotates you in a counter-clockwise dir

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490917657868

Tags
#matlab #programming
Question
The function view takes two arguments. The first one, az in this example, is called the azimuth or polar angle in the x-y plane (in degrees). az rotates the viewpoint (you) about the z-axis—i.e. about the ‘pinnacle’ at (15,15) in Fig- ure 9.12—in a counter-clockwise direction. The default value of az is −37.5 ◦ . The program therefore rotates you in a counter-clockwise direction about the z-axis in 15 ◦ steps starting at the default position. The second argument of view is the [...] el (in degrees). This is the angle a line from the viewpoint makes with the x-y plane. A value of 90 ◦ for el means you are directly overhead. Positive values of the elevation mean you are above the x-y plane; negative values mean you are below it. The default value of el is 30 ◦
Answer
vertical elevation


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
9.12—in a counter-clockwise direction. The default value of az is −37.5 ◦ . The program therefore rotates you in a counter-clockwise direction about the z-axis in 15 ◦ steps starting at the default position. The second argument of view is the <span>vertical elevation el (in degrees). This is the angle a line from the viewpoint makes with the x-y plane. A value of 90 ◦ for el means you are directly overhead. Positive values of the elevation mean you

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490919230732

Tags
#biochem #biology #cell
Question
the [...] protein, an essential nuclear protein with roles in the tran- scription, processing, and transport of specific mRNA molecules.
Answer
FUS


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
the FUS protein, an essential nuclear protein with roles in the tran- scription, processing, and transport of specific mRNA molecules.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490920803596

Tags
#biochem #biology #cell
Question
the FUS protein, [...]
Answer
an essential nuclear protein with roles in the tran- scription, processing, and transport of specific mRNA molecules.


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
the FUS protein, an essential nuclear protein with roles in the tran- scription, processing, and transport of specific mRNA molecules.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490922376460

Tags
#biochem #biology #cell
Question
Over [...] of its C-terminal domain of two hundred amino acids is composed of only four amino acids: glycine, serine, glutamine, and tyrosine. This low complexity domain is attached to several other domains that bind to RNA molecules.
Answer
80 per- cent


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Over 80 per- cent of its C-terminal domain of two hundred amino acids is composed of only four amino acids: glycine, serine, glutamine, and tyrosine. This low complexity domain is attached to several o

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490923949324

Tags
#biochem #biology #cell
Question
Over 80 per- cent of its [...] domain of two hundred amino acids is composed of only four amino acids: glycine, serine, glutamine, and tyrosine. This low complexity domain is attached to several other domains that bind to RNA molecules.
Answer
C-terminal


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Over 80 per- cent of its C-terminal domain of two hundred amino acids is composed of only four amino acids: glycine, serine, glutamine, and tyrosine. This low complexity domain is attached to several other domains that

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490925522188

Tags
#biochem #biology #cell
Question
Over 80 per- cent of its C-terminal domain of [...] amino acids is composed of only four amino acids: glycine, serine, glutamine, and tyrosine. This low complexity domain is attached to several other domains that bind to RNA molecules.
Answer
two hundred


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Over 80 per- cent of its C-terminal domain of two hundred amino acids is composed of only four amino acids: glycine, serine, glutamine, and tyrosine. This low complexity domain is attached to several other domains that bind to RNA molecules.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490927095052

Tags
#biochem #biology #cell
Question
Over 80 per- cent of its C-terminal domain of two hundred amino acids is composed of only four amino acids: [...] This low complexity domain is attached to several other domains that bind to RNA molecules.
Answer
glycine, serine, glutamine, and tyrosine.


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Over 80 per- cent of its C-terminal domain of two hundred amino acids is composed of only four amino acids: glycine, serine, glutamine, and tyrosine. This low complexity domain is attached to several other domains that bind to RNA molecules.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490929454348

Tags
#biochem #biology #cell
Question
At high enough concentrations in a test tube, [...] forms a hydrogel that will associate with either itself or with the low complexity domains from other proteins.
Answer
FUS protein


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
At high enough concentrations in a test tube, FUS protein forms a hydrogel that will associate with either itself or with the low complexity domains from other proteins.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490931027212

Tags
#biochem #biology #cell
Question
At high enough concentrations in a test tube, FUS protein forms a [...] that will associate with either itself or with the low complexity domains from other proteins.
Answer
hydrogel


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
At high enough concentrations in a test tube, FUS protein forms a hydrogel that will associate with either itself or with the low complexity domains from other proteins.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1490932600076

Tags
#biochem #biology #cell
Question
At high enough concentrations in a test tube, FUS protein forms a hydrogel that will [...]
Answer
associate with either itself or with the low complexity domains from other proteins.


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
At high enough concentrations in a test tube, FUS protein forms a hydrogel that will associate with either itself or with the low complexity domains from other proteins.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491029069068

Tags
#biochem #biology #cell
Question
he [...]s a long unbranched structure assembled through a repeating aggre- gate of β sheets
Answer
amyloid fibril i


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
he amyloid fibril is a long unbranched structure assembled through a repeating aggre- gate of β sheets

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491042176268

Tags
#biochem #biology #cell
Question
he amyloid fibril is a [...]
Answer
long unbranched structure assembled through a repeating aggre- gate of β sheets


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
he amyloid fibril is a long unbranched structure assembled through a repeating aggre- gate of β sheets

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491060526348

Tags
#biochem #biology #cell
Question
The amino acids located at [...] have been the slowest to change during the long evolutionary process
Answer
the binding site for the phosphorylated polypeptide


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The amino acids located at the binding site for the phosphorylated polypeptide have been the slowest to change during the long evolutionary process

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491069439244

Tags
#biochem #biology #cell
Question
A detailed examination of the antigen-binding sites of antibodies reveals that they are formed from [...]
Answer
several loops of polypeptide chain that protrude from the ends of a pair of closely juxtaposed protein domains


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A detailed examination of the antigen-binding sites of antibodies reveals that they are formed from several loops of polypeptide chain that protrude from the ends of a pair of closely juxtaposed protein domains

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491073109260

Tags
#biochem #biology #cell
Question
A detailed examination of the [...] reveals that they are formed from several loops of polypeptide chain that protrude from the ends of a pair of closely juxtaposed protein domains
Answer
antigen-binding sites of antibodies


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A detailed examination of the antigen-binding sites of antibodies reveals that they are formed from several loops of polypeptide chain that protrude from the ends of a pair of closely juxtaposed protein domains

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491078876428

Tags
#biochem #biology #cell
Question
[...] are Y-shaped molecules with two identical binding sites that are complementary to a small portion of the surface of the antigen molecule
Answer
Antibodies


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Antibodies are Y-shaped molecules with two identical binding sites that are complementary to a small portion of the surface of the antigen molecule

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491090935052

Tags
#biochem #biology #cell
Question
Antibodies are [shape?] molecules with two identical binding sites that are complementary to a small portion of the surface of the antigen molecule
Answer
Y-shaped


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Antibodies are Y-shaped molecules with two identical binding sites that are complementary to a small portion of the surface of the antigen molecule

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491095653644

Tags
#biochem #biology #cell
Question
Antibodies are Y-shaped molecules with [...] identical binding sites that are complementary to a small portion of the surface of the antigen molecule
Answer
two


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Antibodies are Y-shaped molecules with two identical binding sites that are complementary to a small portion of the surface of the antigen molecule

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491100372236

Tags
#biochem #biology #cell
Question
Antibodies are Y-shaped molecules with two identical binding sites that are [complementary to what?]
Answer
complementary to a small portion of the surface of the antigen molecule


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Antibodies are Y-shaped molecules with two identical binding sites that are complementary to a small portion of the surface of the antigen molecule

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Systeemiteoreettisesti 1 ajatellen klassisissa organisaationäkemyksissä organisaatioita tarkasteltiin suljettuina järjestelminä. Suljetun organisaation malli soveltui systeemiteorian mukaan hitaasti muuttuviin oloihin, joissa oli vain yksinkertaisia ongelmia ratkaistavana. Klassisen organisaationäkemyksen viitekehys soveltui lisäksi tilanteisiin, joissa oli käytettävissä vain vähäisen koulutuksen saanutta henkilöstöä ja alkeellista tekniikkaa.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Simonin (1958) mukaan ihmiset eivät tehneet päätöksiä pyrkien rationaalisesti optimoimaan tuloksia. Sen sijaan ihmiset tekivät päätöksiä, jotka johtivat tyydyttäviin ratkaisuihin. Simon väittikin, että ihmiset tekevät päätöksiä rajoitetun rationaalisuuden (bounded rationality) mukaisesti.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Selznick (1957) puhui kirjoissaan institutionalisoitumisprosessista, jolla hän tarkoitti sitä, että ihmiset tuovat mukanaan organisaatioon aina omia ajattelu-ja toimintamallejaan. Näin rationaalisista systeemeistä tulee sosiaalisia järjestelmiä. Sosiaalisessa järjestelmässä vaikuttavat muun muassa sellaiset seikat kuin ihmisten tunteet ja kokemukset sekä heidän keskinäiset suhteensa.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Klassisissa organisaatioteorioissa pidettiin organisaatiorakenteeseen liittyviä seikkoja tutkimuksen keskiössä. Viime vuosisadan alkuvuosina pohdittiin paljon sitä, kuinka tulisi organisoitua, jotta voitaisiin saavuttaa mahdollisimman suuri tehokkuus

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491358584076

Tags
#deeplearning #neuralnetworks
Question
[...] : \(D_{KL}( P||Q) = E_{x\sim P} = [log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \) (3.50) In the case of discrete v ariables, it is the extra amount of information (measured in bits if we use the base 2 logarithm, (but in machine learning w e usually use nats and the natural logarithm) needed to send a message containing symbols drawn from probability distribution P , when w e use a co de that w as designed to minimize the length of messages dra wn from probabilit y distribution Q.
Answer
Kullbac k-Leibler (KL) div ergence


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Kullbac k-Leibler (KL) div ergence : \(D_{KL}( P||Q) = E_{x\sim P} = [log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \) (3.50) In the case of discrete v ariables, it is the extra amount of information

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491360156940

Tags
#deeplearning #neuralnetworks
Question
Kullbac k-Leibler (KL) div ergence : [...] (3.50) In the case of discrete v ariables, it is the extra amount of information (measured in bits if we use the base 2 logarithm, (but in machine learning w e usually use nats and the natural logarithm) needed to send a message containing symbols drawn from probability distribution P , when w e use a co de that w as designed to minimize the length of messages dra wn from probabilit y distribution Q.
Answer
\(D_{KL}( P||Q) = E_{x\sim P} [log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Kullbac k-Leibler (KL) div ergence : \(D_{KL}( P||Q) = E_{x\sim P} = [log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \) (3.50) In the case of discrete v ariables, it is the extra amount of information (measured in bits if we use the base 2 logarithm, (but in machine learning w e usually use nats and the

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491361729804

Tags
#deeplearning #neuralnetworks
Question
Kullbac k-Leibler (KL) div ergence : \(D_{KL}( P||Q) = E_{x\sim P} = [log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \) (3.50) In the case of discrete v ariables, it is the [...] (measured in bits if we use the base 2 logarithm, (but in machine learning w e usually use nats and the natural logarithm) [...]
Answer
extra amount of information needed to send a message containing symbols drawn from probability distribution P , when w e use a co de that w as designed to minimize the length of messages dra wn from probabilit y distribution Q.


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Kullbac k-Leibler (KL) div ergence : \(D_{KL}( P||Q) = E_{x\sim P} = [log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \) (3.50) In the case of discrete v ariables, it is the extra amount of information (measured in bits if we use the base 2 logarithm, (but in machine learning w e usually use nats and the natural logarithm) needed to send a message containing symbols drawn from probabi

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491363302668

Tags
#deeplearning #neuralnetworks
Question
Kullbac k-Leibler (KL) div ergence : \(D_{KL}( P||Q) = E_{x\sim P} = [log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \) (3.50) In the case of discrete v ariables, it is the extra amount of information (measured in [...] if we use the base 2 logarithm, (but in machine learning w e usually use nats and the natural logarithm) needed to send a message containing symbols drawn from probability distribution P , when w e use a co de that w as designed to minimize the length of messages dra wn from probabilit y distribution Q.
Answer
bits


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
pan>Kullbac k-Leibler (KL) div ergence : \(D_{KL}( P||Q) = E_{x\sim P} = [log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \) (3.50) In the case of discrete v ariables, it is the extra amount of information (measured in bits if we use the base 2 logarithm, (but in machine learning w e usually use nats and the natural logarithm) needed to send a message containing symbols drawn from probability distribution

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491365661964

Tags
#deeplearning #neuralnetworks
Question
Kullbac k-Leibler (KL) div ergence : \(D_{KL}( P||Q) = E_{x\sim P} = [log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \) (3.50) In the case of discrete v ariables, it is the extra amount of information (measured in bits if we use the base 2 logarithm, (but in machine learning w e usually use [...]) needed to send a message containing symbols drawn from probability distribution P , when w e use a co de that w as designed to minimize the length of messages dra wn from probabilit y distribution Q.
Answer
nats and the natural logarithm


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
[log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \) (3.50) In the case of discrete v ariables, it is the extra amount of information (measured in bits if we use the base 2 logarithm, (but in machine learning w e usually use <span>nats and the natural logarithm) needed to send a message containing symbols drawn from probability distribution P , when w e use a co de that w as designed to minimize the length of messages dra wn from probabilit y

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491369331980

Tags
#deeplearning #neuralnetworks
Question
[...] is non-negativ e and measures the difference b et w een t w o distributions, it is often conceptualized as measuring some sort of distance b etw een these distributions. How ever, it is not a true distance measure b ecause it is not symmetric
Answer
KL div ergence


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
KL div ergence is non-negativ e and measures the difference b et w een t w o distributions, it is often conceptualized as measuring some sort of distance b etw een these distributions. How ever, it is

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491371166988

Tags
#deeplearning #neuralnetworks
Question
KL div ergence is [...] and measures the difference b et w een t w o distributions, it is often conceptualized as measuring some sort of distance b etw een these distributions. How ever, it is not a true distance measure b ecause it is not symmetric
Answer
non-negativ e


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
KL div ergence is non-negativ e and measures the difference b et w een t w o distributions, it is often conceptualized as measuring some sort of distance b etw een these distributions. How ever, it is not a true distan

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491372739852

Tags
#deeplearning #neuralnetworks
Question
KL div ergence is non-negativ e and measures the [...], it is often conceptualized as measuring some sort of distance b etw een these distributions. How ever, it is not a true distance measure b ecause it is not symmetric
Answer
difference b et w een t w o distributions


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
KL div ergence is non-negativ e and measures the difference b et w een t w o distributions, it is often conceptualized as measuring some sort of distance b etw een these distributions. How ever, it is not a true distance measure b ecause it is not symmetric</bod

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491374574860

Tags
#deeplearning #neuralnetworks
Question
KL div ergence is non-negativ e and measures the difference b et w een t w o distributions, it is often conceptualized as [...]. How ever, it is not a true distance measure b ecause it is not symmetric
Answer
measuring some sort of distance b etw een these distributions


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
KL div ergence is non-negativ e and measures the difference b et w een t w o distributions, it is often conceptualized as measuring some sort of distance b etw een these distributions. How ever, it is not a true distance measure b ecause it is not symmetric

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491376147724

Tags
#deeplearning #neuralnetworks
Question
KL div ergence is non-negativ e and measures the difference b et w een t w o distributions, it is often conceptualized as measuring some sort of distance b etw een these distributions. How ever, it is not a true distance measure b ecause it is [...]
Answer
not symmetric


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
KL div ergence is non-negativ e and measures the difference b et w een t w o distributions, it is often conceptualized as measuring some sort of distance b etw een these distributions. How ever, it is not a true distance measure b ecause it is <span>not symmetric<span><body><html>

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Klassisessa organisaatioteoriassa tehokkuudella tarkoitettiin organisaation sisäistä tehokkuutta. Tehokkuutta pyrittiin parantamaan osittamalla tehtävät mahdollisimman pitkälle ja luomalla tarkasti rajattuja tehtäviä. Lisäksi pyrittiin laatimaan säännöt ja toimintaohjeet,

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491379293452

Tags
#deeplearning #neuralnetworks
Question
A quan tit y that is closely related to the [...] is the cross-en trop y H ( P , Q ) = H ( P ) + D KL ( P Q ) , whic h is similar to the KL div ergence but lac king the term on the left: H P, Q ( ) = − E x ∼ P log ( ) Q x
Answer
KL div ergence


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A quan tit y that is closely related to the KL div ergence is the cross-en trop y H ( P , Q ) = H ( P ) + D KL ( P Q ) , whic h is similar to the KL div ergence but lac king the term on the left: H P, Q ( ) = − E x ∼ P log ( ) Q x</sp

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491380866316

Tags
#deeplearning #neuralnetworks
Question
A quan tit y that is closely related to the KL div ergence is the [...] H ( P , Q ) = H ( P ) + D KL ( P Q ) , whic h is similar to the KL div ergence but lac king the term on the left: H P, Q ( ) = − E x ∼ P log ( ) Q x
Answer
cross-en trop y


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A quan tit y that is closely related to the KL div ergence is the cross-en trop y H ( P , Q ) = H ( P ) + D KL ( P Q ) , whic h is similar to the KL div ergence but lac king the term on the left: H P, Q ( ) = − E x ∼ P log ( ) Q x

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
joiden mukaan kussakin toimessa ja tehtävässä tuli toimia. Organisaatiosta luotiin hierarkkinen järjestelmä, jossa ylemmät tahot valvoivat alempien tasojen toimintaa.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Klassisen organisaatioteorian periaatteita olivat muun muassa seuraavat: • Pitkälle viety työnjako: jokainen toiminto tuli jakaa tehtäviin ja toimiin. Jokaiseen tehtävään tuli valita sellainen henkilö, jolla oli koulutus kyseiseen tehtävään. • Työnjaon ja vastuunjaon selkeys: jokaisen henkilön tuli tietää, mitä häneltä odotettiin, ja jokaiseen tehtävään tuli laatia selvä ohje, kuinka työt oli tehtävä. • Hierarkkinen valvonta: jokaisen tehtävän tuli olla ylemmän tason valvonnan ja ohjauksen alla. Kullakin henkilöllä tuli olla vain yksi esimies. • Kapea valvontajänne: kullakin esimiehellä voi olla vain muutamia alaisia, jotta esimies kykeni valvomaan ja ohjaamaan alaisten työskentelyä

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491392924940

Tags
#deeplearning #neuralnetworks
Question
A quan tit y that is closely related to the KL div ergence is the cross-en trop y [...] , whic h is similar to the KL div ergence but lac king the term on the left: \(H (P, Q ) = − E _{x \sim P} \space log Q(x)\)
Answer
\(H ( P , Q ) = H ( P ) + D_{KL}( P|| Q )\)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A quan tit y that is closely related to the KL div ergence is the cross-en trop y \(H ( P , Q ) = H ( P ) + D_{KL}( P|| Q )\) , whic h is similar to the KL div ergence but lac king the term on the left: \(H (P, Q ) = − E _{x \sim P} \space log Q(x)\)

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491394759948

Tags
#deeplearning #neuralnetworks
Question
A quan tit y that is closely related to the KL div ergence is the cross-en trop y \(H ( P , Q ) = H ( P ) + D_{KL}( P|| Q )\) , whic h is similar to the KL div ergence but [...]: \(H (P, Q ) = − E _{x \sim P} \space log Q(x)\)
Answer
lac king the term on the left


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A quan tit y that is closely related to the KL div ergence is the cross-en trop y \(H ( P , Q ) = H ( P ) + D_{KL}( P|| Q )\) , whic h is similar to the KL div ergence but lac king the term on the left: \(H (P, Q ) = − E _{x \sim P} \space log Q(x)\)

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491398429964

Tags
#deeplearning #neuralnetworks
Question
There are t w o main kinds of structured probabilistic mo dels: [...]
Answer
directed and undirected.


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
There are t w o main kinds of structured probabilistic mo dels: directed and undirected.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491400264972

Tags
#deeplearning #neuralnetworks
Question
There are t w o main kinds of [...]: directed and undirected.
Answer
structured probabilistic mo dels


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
There are t w o main kinds of structured probabilistic mo dels: directed and undirected.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491402362124

Tags
#deeplearning #neuralnetworks
Question
[...] mo dels use graphs with directed edges, and they represen t fac- torizations into conditional probability distributions
Answer
Directed


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Directed mo dels use graphs with directed edges, and they represen t fac- torizations into conditional probability distributions

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491404459276

Tags
#deeplearning #neuralnetworks
Question
Directed mo dels use [...], and they represen t fac- torizations into conditional probability distributions
Answer
graphs with directed edges


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Directed mo dels use graphs with directed edges, and they represen t fac- torizations into conditional probability distributions

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491406032140

Tags
#deeplearning #neuralnetworks
Question
Directed mo dels use graphs with directed edges, and they represen t [...]
Answer
fac- torizations into conditional probability distributions


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Directed mo dels use graphs with directed edges, and they represen t fac- torizations into conditional probability distributions

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491407867148

Tags
#deeplearning #neuralnetworks
Question
[...] mo dels use graphs with undirected edges, and they represen t factorizations into a set of functions
Answer
Undirected


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Undirected mo dels use graphs with undirected edges, and they represen t factorizations into a set of functions

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491409702156

Tags
#deeplearning #neuralnetworks
Question
Undirected mo dels use [...] and they represen t factorizations into a set of functions
Answer
graphs with undirected edges,


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Undirected mo dels use graphs with undirected edges, and they represen t factorizations into a set of functions

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491411537164

Tags
#deeplearning #neuralnetworks
Question
Undirected mo dels use graphs with undirected edges, and they represen t [...]
Answer
factorizations into a set of functions


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Undirected mo dels use graphs with undirected edges, and they represen t factorizations into a set of functions

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491413372172

Tags
#deeplearning #neuralnetworks
Question
Any set of no des that are all connected to each other in G is called a [...]
Answer
clique.


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Any set of no des that are all connected to each other in G is called a clique.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491415207180

Tags
#deeplearning #neuralnetworks
Question
Any [...] is called a clique.
Answer
set of no des that are all connected to each other in G


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Any set of no des that are all connected to each other in G is called a clique.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491417042188

Tags
#deeplearning #neuralnetworks
Question
[...] o ccurs when n um b ers near zero are rounded to zero
Answer
Underflo w


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Underflo w o ccurs when n um b ers near zero are rounded to zero

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491418877196

Tags
#deeplearning #neuralnetworks
Question
Underflo w o ccurs when [...]
Answer
n um b ers near zero are rounded to zero


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Underflo w o ccurs when n um b ers near zero are rounded to zero

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491422285068

Tags
#deeplearning #neuralnetworks
Question
[...] o ccurs when n um bers with large magnitude are appro ximated as ∞ or −∞
Answer
Overflo w


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Overflo w o ccurs when n um bers with large magnitude are appro ximated as ∞ or −∞

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491423857932

Tags
#deeplearning #neuralnetworks
Question
Overflo w o ccurs when [...]
Answer
n um bers with large magnitude are appro ximated as ∞ or −∞


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Overflo w o ccurs when n um bers with large magnitude are appro ximated as ∞ or −∞

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Elton!Mayon!Hawthorne>tutkimukset!(1920>luvulla):! Työtuloksen!kohenemiseen!vaikuDavat! –  Sosiaalisten!olojen!parantuminen! –  Työntekijöiden!saama!huomio!ylipäänsä!,!”Hawthorne> efek&”!

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491429887244

Tags
#deeplearning #neuralnetworks
Question
Both of these difficulties can b e resolved by instead [...] where z = x − maxi xi . Simple algebra shows that the v alue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtracting max i x i results in the largest argument to exp b eing 0, whic h rules out the p ossibility of ov erflo w. Lik ewise, at least one term in the denominator has a v alue of 1, which rules out the p ossibility of underflow in the denominator leading to a division by zero
Answer
ev aluating softmax ( z )


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Both of these difficulties can b e resolved by instead ev aluating softmax ( z ) where z = x − max i x i . Simple algebra shows that the v alue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtractin

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491431460108

Tags
#deeplearning #neuralnetworks
Question
Both of these difficulties can b e resolved by instead ev aluating softmax ( z ) where z = [...] Simple algebra shows that the v alue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtracting max i x i results in the largest argument to exp b eing 0, whic h rules out the p ossibility of ov erflo w. Lik ewise, at least one term in the denominator has a v alue of 1, which rules out the p ossibility of underflow in the denominator leading to a division by zero
Answer
x − maxi xi .


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Both of these difficulties can b e resolved by instead ev aluating softmax ( z ) where z = x − max i x i . Simple algebra shows that the v alue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtracting max i x i results in the l

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491434343692

Tags
#deeplearning #neuralnetworks
Question
Both of these difficulties can b e resolved by instead ev aluating softmax ( z ) where z = x − maxi xi . Simple algebra shows that the v alue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtracting max i x i results in [...], whic h rules out the p ossibility of ov erflo w. Lik ewise, at least one term in the denominator has a v alue of 1, which rules out the p ossibility of underflow in the denominator leading to a division by zero
Answer
the largest argument to exp b eing 0


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
instead ev aluating softmax ( z ) where z = x − max i x i . Simple algebra shows that the v alue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtracting max i x i results in <span>the largest argument to exp b eing 0, whic h rules out the p ossibility of ov erflo w. Lik ewise, at least one term in the denominator has a v alue of 1, which rules out the p ossibility of underflow in the denominator leadi

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491436178700

Tags
#deeplearning #neuralnetworks
Question
Both of these difficulties can b e resolved by instead ev aluating softmax ( z ) where z = x − maxi xi . Simple algebra shows that the v alue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtracting max i x i results in the largest argument to exp b eing 0, whic h [...]. Lik ewise, at least one term in the denominator has a v alue of 1, which rules out the p ossibility of underflow in the denominator leading to a division by zero
Answer
rules out the p ossibility of ov erflo w


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
x − max i x i . Simple algebra shows that the v alue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtracting max i x i results in the largest argument to exp b eing 0, whic h <span>rules out the p ossibility of ov erflo w. Lik ewise, at least one term in the denominator has a v alue of 1, which rules out the p ossibility of underflow in the denominator leading to a division by zero<span><body></h

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491437751564

Tags
#deeplearning #neuralnetworks
Question
Both of these difficulties can b e resolved by instead ev aluating softmax ( z ) where z = x − maxi xi . Simple algebra shows that the v alue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtracting max i x i results in the largest argument to exp b eing 0, whic h rules out the p ossibility of ov erflo w. Lik ewise, [...], which rules out the p ossibility of underflow in the denominator leading to a division by zero
Answer
at least one term in the denominator has a v alue of 1


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
ue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtracting max i x i results in the largest argument to exp b eing 0, whic h rules out the p ossibility of ov erflo w. Lik ewise, <span>at least one term in the denominator has a v alue of 1, which rules out the p ossibility of underflow in the denominator leading to a division by zero<span><body><html>

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491439586572

Tags
#deeplearning #neuralnetworks
Question
Both of these difficulties can b e resolved by instead ev aluating softmax ( z ) where z = x − maxi xi . Simple algebra shows that the v alue of the softmax function is not c hanged analytically by adding or subtracting a scalar from the input vector. Subtracting max i x i results in the largest argument to exp b eing 0, whic h rules out the p ossibility of ov erflo w. Lik ewise, at least one term in the denominator has a v alue of 1, which rules out the p ossibility of [...] in the denominator leading to a division by zero
Answer
underflow


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
om the input vector. Subtracting max i x i results in the largest argument to exp b eing 0, whic h rules out the p ossibility of ov erflo w. Lik ewise, at least one term in the denominator has a v alue of 1, which rules out the p ossibility of <span>underflow in the denominator leading to a division by zero<span><body><html>

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491441159436

Tags
#deeplearning #neuralnetworks
Question
W e usually phrase most optimization problems in terms of [...] . Maximization ma y b e accomplished via a minimization algorithm by minimizing − f ( ) x
Answer
minimizing f ( x )


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
W e usually phrase most optimization problems in terms of minimizing f ( x ) . Maximization ma y b e accomplished via a minimization algorithm by minimizing − f ( ) x

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491442732300

Tags
#deeplearning #neuralnetworks
Question
W e usually phrase most optimization problems in terms of minimizing f ( x ) . Maximization ma y b e accomplished via a [...]
Answer
minimization algorithm by minimizing − f (x)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
W e usually phrase most optimization problems in terms of minimizing f ( x ) . Maximization ma y b e accomplished via a minimization algorithm by minimizing − f ( ) x

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491445878028

Tags
#deeplearning #neuralnetworks
Question
W e often denote the v alue that minimizes or maximizes a function with a [...] . F or example, w e might say ∗
Answer
sup erscript


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
W e often denote the v alue that minimizes or maximizes a function with a sup erscript . F or example, w e might say ∗

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491447450892

Tags
#deeplearning #neuralnetworks
Question
A [...] is a p oint where f ( x ) is higher than at all neighboring p oin ts
Answer
lo cal maximum


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A lo cal maximum is a p oint where f ( x ) is higher than at all neighboring p oin ts

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491449023756

Tags
#deeplearning #neuralnetworks
Question
A lo cal maximum is a p oint where [...]
Answer
f ( x ) is higher than at all neighboring p oin ts


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A lo cal maximum is a p oint where f ( x ) is higher than at all neighboring p oin ts

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491450858764

Tags
#deeplearning #neuralnetworks
Question
Some critical p oin ts are neither maxima nor minima. These are kno wn as [...]
Answer
saddle p oints


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Some critical p oin ts are neither maxima nor minima. These are kno wn as saddle p oints

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491452693772

Tags
#deeplearning #neuralnetworks
Question
Some critical p oin ts are [...]. These are kno wn as saddle p oints
Answer
neither maxima nor minima


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Some critical p oin ts are neither maxima nor minima. These are kno wn as saddle p oints

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491455053068

Tags
#bayes #programming #r #statistics
Question
Notice what we must be able to do in the random-walk process: • We must be able to [...] . • We must be able to evaluate the target distribution at any proposed position, to compute P(θ proposed )/P(θ current ). • We must be able to generate a random value from a uniform distribution, to accept or reject the proposal according to p move
Answer
generate a random value from the proposal distribution, to create θ proposed


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Notice what we must be able to do in the random-walk process: • We must be able to generate a random value from the proposal distribution, to create θ proposed . • We must be able to evaluate the target distribution at any proposed position, to compute P(θ proposed )/P(θ current ). • We must be able to generate a random value from a uniform di

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491456888076

Tags
#bayes #programming #r #statistics
Question
Notice what we must be able to do in the random-walk process: • We must be able to generate a random value from the proposal distribution, to create θ proposed . • We must be able to [...], to compute P(θ proposed )/P(θ current ). • We must be able to generate a random value from a uniform distribution, to accept or reject the proposal according to p move
Answer
evaluate the target distribution at any proposed position


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Notice what we must be able to do in the random-walk process: • We must be able to generate a random value from the proposal distribution, to create θ proposed . • We must be able to evaluate the target distribution at any proposed position, to compute P(θ proposed )/P(θ current ). • We must be able to generate a random value from a uniform distribution, to accept or reject the proposal according to p move</b

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491458723084

Tags
#bayes #programming #r #statistics
Question
Notice what we must be able to do in the random-walk process: • We must be able to generate a random value from the proposal distribution, to create θ proposed . • We must be able to evaluate the target distribution at any proposed position, to [...]. • We must be able to generate a random value from a uniform distribution, to accept or reject the proposal according to p move
Answer
compute P(θ proposed )/P(θ current )


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
tice what we must be able to do in the random-walk process: • We must be able to generate a random value from the proposal distribution, to create θ proposed . • We must be able to evaluate the target distribution at any proposed position, to <span>compute P(θ proposed )/P(θ current ). • We must be able to generate a random value from a uniform distribution, to accept or reject the proposal according to p move<span><body><html>

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491460558092

Tags
#bayes #programming #r #statistics
Question
Notice what we must be able to do in the random-walk process: • We must be able to generate a random value from the proposal distribution, to create θ proposed . • We must be able to evaluate the target distribution at any proposed position, to compute P(θ proposed )/P(θ current ). • We must be able to [...], to accept or reject the proposal according to p move
Answer
generate a random value from a uniform distribution


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
• We must be able to generate a random value from the proposal distribution, to create θ proposed . • We must be able to evaluate the target distribution at any proposed position, to compute P(θ proposed )/P(θ current ). • We must be able to <span>generate a random value from a uniform distribution, to accept or reject the proposal according to p move<span><body><html>

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491462130956

Tags
#bayes #programming #r #statistics
Question
Notice what we must be able to do in the random-walk process: • We must be able to generate a random value from the proposal distribution, to create θ proposed . • We must be able to evaluate the target distribution at any proposed position, to compute P(θ proposed )/P(θ current ). • We must be able to generate a random value from a uniform distribution, to [...]
Answer
accept or reject the proposal according to p move


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
roposal distribution, to create θ proposed . • We must be able to evaluate the target distribution at any proposed position, to compute P(θ proposed )/P(θ current ). • We must be able to generate a random value from a uniform distribution, to <span>accept or reject the proposal according to p move<span><body><html>

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491464228108

Tags
#bayes #programming #r #statistics
Question
Suppose we are at position θ. The probability of moving to θ + 1, denoted p(θ → θ + 1), is the [...], which is p(θ → θ + 1) =0.5 · min ( P(θ + 1)/P(θ ),1 )
Answer
probability of proposing that move times the probability of accepting it if proposed


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Suppose we are at position θ. The probability of moving to θ + 1, denoted p(θ → θ + 1), is the probability of proposing that move times the probability of accepting it if proposed, which is p(θ → θ + 1) =0.5 · min ( P(θ + 1)/P(θ ),1 )

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491466063116

Tags
#bayes #programming #r #statistics
Question
Suppose we are at position θ. The probability of moving to θ + 1, denoted p(θ → θ + 1), is the probability of proposing that move times the probability of accepting it if proposed, which is [...]
Answer
p(θ → θ + 1) =0.5 · min ( P(θ + 1)/P(θ ),1 )


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Suppose we are at position θ. The probability of moving to θ + 1, denoted p(θ → θ + 1), is the probability of proposing that move times the probability of accepting it if proposed, which is p(θ → θ + 1) =0.5 · min ( P(θ + 1)/P(θ ),1 )

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491467898124

Tags
#matlab #programming
Question
Here’s how to get [...]: The functions that draw graphics objects can also be used to return the handle of the object drawn, e.g., x = 0:pi/20:2*pi; hsin = plot(x, sin(x)) hold on hx = xlabel(’x’)
Answer
handles


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Here’s how to get handles: The functions that draw graphics objects can also be used to return the handle of the object drawn, e.g., x = 0:pi/20:2*pi; hsin = plot(x, sin(x)) hold on hx = xlabel(’x’)</sp

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491469733132

Tags
#matlab #programming
Question
Here’s how to get handles: [...], e.g., x = 0:pi/20:2*pi; hsin = plot(x, sin(x)) hold on hx = xlabel(’x’)
Answer
The functions that draw graphics objects can also be used to return the handle of the object drawn


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Here’s how to get handles: The functions that draw graphics objects can also be used to return the handle of the object drawn, e.g., x = 0:pi/20:2*pi; hsin = plot(x, sin(x)) hold on hx = xlabel(’x’)

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491472878860

Tags
#matlab #programming
Question
[...] gets the handle of the current graphics object, which is the last graph- ics object created or clicked on. For example, draw the sine graph above and get its handle hsin.
Answer
gco


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
gco gets the handle of the current graphics object, which is the last graph- ics object created or clicked on. For example, draw the sine graph above and get its handle hsin. Click on the g

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491474976012

Tags
#matlab #programming
Question
gco [...], which is the last graph- ics object created or clicked on. For example, draw the sine graph above and get its handle hsin. Click on the graph in the figure win- dow. Then enter the command ho = gco
Answer
gets the handle of the current graphics object


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
gco gets the handle of the current graphics object, which is the last graph- ics object created or clicked on. For example, draw the sine graph above and get its handle hsin. Click on the graph in the figure win- dow. Then enter the com

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491476811020

Tags
#matlab #programming
Question
The command [...] lists all the possible property values (where appro- priate)
Answer
set(handle)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The command set(handle) lists all the possible property values (where appro- priate)

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491478383884

Tags
#matlab #programming
Question
The command set(handle) [...]
Answer
lists all the possible property values (where appro- priate)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The command set(handle) lists all the possible property values (where appro- priate)

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Rationaalisuudella Weber tarkoitti prosessia, jossa ennalta määritellyt, laskettavissa olevat säännöt ja menettelytavat korvasivat tunteet, perinteet ja totutut tavat toiminnan ohjauksessa. Weberin mukaan byrokratian etuna oli tehokkuus, joka perustui juuri rationaalisuuden varaan.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491482578188

Tags
#matlab #programming
Question
If a graphics object has a number of children the [...] command used with the children property returns a vector of the children’s handles
Answer
get


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
If a graphics object has a number of children the get command used with the children property returns a vector of the children’s handles

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491484151052

Tags
#matlab #programming
Question
If a graphics object has a number of children the get command used with the children property returns [...]
Answer
a vector of the children’s handles


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
If a graphics object has a number of children the get command used with the children property returns a vector of the children’s handles

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Byrokratiaa oli helppo kuvailla ja käyttää ihannemallina, sillä se on organisaatiomuoto, joka on ollut kirkon ja armeijan käytössä jo paljon ennen organisaatioteorioiden syntyä.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491488083212

Tags
#biochem #biology #cell
Question
From the concentrations of the [...] at equilibrium, we can calculate a convenient measure of the strength of bind- ing—the equilibrium constant (K)
Answer
ligand, antibody, and antibody–ligand complex


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
From the concentrations of the ligand, antibody, and antibody–ligand complex at equilibrium, we can calculate a convenient measure of the strength of bind- ing—the equilibrium constant (K)

Original toplevel document (pdf)

cannot see any pdfs







#1 #2 #anki #byrokratian_ihannemalli #klassiset_koulukunnat #lk1
tehokkuus aiheutui siitä, että virkoihin valittiin niihin koulutuksen saaneita henkilöitä

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491491491084

Tags
#biochem #biology #cell
Question
From the concentrations of the ligand, antibody, and antibody–ligand complex at equilibrium, we can calculate a [...]
Answer
convenient measure of the strength of bind- ing—the equilibrium constant (K)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
From the concentrations of the ligand, antibody, and antibody–ligand complex at equilibrium, we can calculate a convenient measure of the strength of bind- ing—the equilibrium constant (K)

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491493850380

Tags
#biochem #biology #cell
Question
pyruvate formed by glycolysis is rapidly transported into the mitochondria, where it is converted into [...], whose acetyl group is then completely oxidized to CO 2 and H 2 O
Answer
CO 2 plus acetyl CoA


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
pyruvate formed by glycolysis is rapidly transported into the mitochondria, where it is converted into CO 2 plus acetyl CoA, whose acetyl group is then completely oxidized to CO 2 and H 2 O

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491495685388

Tags
#biochem #biology #cell
Question
[...] formed by glycolysis is rapidly transported into the mitochondria, where it is converted into CO 2 plus acetyl CoA, whose acetyl group is then completely oxidized to CO 2 and H 2 O
Answer
pyruvate


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
pyruvate formed by glycolysis is rapidly transported into the mitochondria, where it is converted into CO 2 plus acetyl CoA, whose acetyl group is then completely oxidized to CO 2 and H 2 O<

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491498568972

Tags
#deeplearning #neuralnetworks
Question
The transp ose of the matrix can be thought of as [...]
Answer
a mirror image across the main diagonal.


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The transp ose of the matrix can be thought of as a mirror image across the main diagonal.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491500403980

Tags
#deeplearning #neuralnetworks
Question
The [...] of the matrix can be thought of as a mirror image across the main diagonal.
Answer
transp ose


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The transp ose of the matrix can be thought of as a mirror image across the main diagonal.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491502763276

Tags
#deeplearning #neuralnetworks
Question
F rom this, we can see that a [...] is its own transp ose: a a =
Answer
scalar


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
F rom this, we can see that a scalar is its own transp ose: a a =

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491504860428

Tags
#deeplearning #neuralnetworks
Question
W e can add matrices to each other, as long as they ha v e the same shap e, just b y [...]: where C A B = + C i,j = A i,j + B i,j
Answer
adding their corresp onding elemen ts


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
W e can add matrices to each other, as long as they ha v e the same shap e, just b y adding their corresp onding elemen ts: where C A B = + C i,j = A i,j + B i,j

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491510627596

Tags
#deeplearning #neuralnetworks
Question
This shorthand eliminates the need to define a matrix with b copied in to eac h ro w b efore doing the addition. This implicit copying of b to man y lo cations is called . [...]
Answer
broadcasting


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
This shorthand eliminates the need to define a matrix with b copied in to eac h ro w b efore doing the addition. This implicit copying of b to man y lo cations is called . broadcasting

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491512724748

Tags
#deeplearning #neuralnetworks
Question
This shorthand eliminates the need to define a matrix with b copied in to eac h ro w b efore doing the addition. This [...] is called . broadcasting
Answer
implicit copying of b to man y lo cations


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
This shorthand eliminates the need to define a matrix with b copied in to eac h ro w b efore doing the addition. This implicit copying of b to man y lo cations is called . broadcasting

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
byrokratian tehokkuus perustui persoonattomille säännöstöille, joiden toimeenpanoa koulutettu henkilöstö toteutti

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491517181196

Tags
#deeplearning #neuralnetworks
Question
Is matrix multiplication is distributive?
Answer
Yes


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
F or example, matrix m ultiplication is distributiv e: A B C AB AC ( + ) = + . (2.6) It is also asso ciativ e: A B C AB C ( ) = ( ) . (2.7) Matrix multiplication is commutativ e (the condition not AB = B A do es not alw a ys hold), unlik

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Byrokratian hyvinä puolina voidaan pitää toimintojen rationaalisuutta ja ennustettavuutta.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491522686220

Tags
#deeplearning #neuralnetworks
Question
Is matrix multiplication associative?
Answer
It is also asso ciativ e


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
F or example, matrix m ultiplication is distributiv e: A B C AB AC ( + ) = + . (2.6) It is also asso ciativ e: A B C AB C ( ) = ( ) . (2.7) Matrix multiplication is commutativ e (the condition not AB = B A do es not alw a ys hold), unlik e scalar multiplication.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491527404812

Tags
#deeplearning #neuralnetworks
Question
Is matrix multiplication commutative?
Answer
No


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
F or example, matrix m ultiplication is distributiv e: A B C AB AC ( + ) = + . (2.6) It is also asso ciativ e: A B C AB C ( ) = ( ) . (2.7) Matrix multiplication is commutativ e (the condition not AB = B A do es not alw a ys hold), unlik e scalar multiplication.

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Byrokratian oletetaan toimivan kuin mekaaninen koneisto, jossa jokainen osanen suorittaa sille annetun tehtävän silloin kuin pitää ja niin kuin pitää

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491535269132

Tags
#biochem #biology #cell
Question
A 70-kg adult human (154 lb) could meet his or her entire energy needs for one day by eating [...]
Answer
3 moles of glucose (540 g)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
A 70-kg adult human (154 lb) could meet his or her entire energy needs for one day by eating 3 moles of glucose (540 g)

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491537628428

Tags
#biochem #biology #cell
Question
The new hydroxyl group on carbon 1 is phosphorylated by ATP, in preparation for the formation of two three-carbon sugar phosphates. The entry of sugars into glycolysis is controlled at this step, through regulation of the enzyme [...]
Answer
phosphofructokinase


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
an> The new hydroxyl group on carbon 1 is phosphorylated by ATP, in preparation for the formation of two three-carbon sugar phosphates. The entry of sugars into glycolysis is controlled at this step, through regulation of the enzyme <span>phosphofructokinase<span><body><html>

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
johdon tarve valvoa toimintoja ja saada ne aiotun kaltaisiksi johtaa siihen, että sääntöjä lisätään ja asetetaan standardeja sille, mikä on hyväksyttävää toimintaa. Tämä puolestaan johtaa siihen, että säännöistä tulee merkittävämpiä käyttäytymiseen vaikuttavia tekijöitä kuin organisaation päämääristä

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491541822732

Tags
#biochem #biology #cell
Question
The six-carbon sugar is cleaved to produce two three-carbon molecules. Only the [...] can proceed immediately through glycolysis.
Answer
glyceraldehyde 3-phosphate


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The six-carbon sugar is cleaved to produce two three-carbon molecules. Only the glyceraldehyde 3-phosphate can proceed immediately through glycolysis.

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Organisaatiot eivät kuitenkaan muodostu mekaanisista osasista, vaan ne ovat ihmisten muodostamia järjestelmiä. Ihminen ajattelee ja toimii itsenäisesti. Ihminen ei myöskään välttämättä toimi aina samalla tavalla eikä niin kuin hänen odotetaan toimivan.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
toiminnasta ja käyttäytymisestä tulee jäykkää

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491547589900

Tags
#biochem #biology #cell
Question
[...] constitute most of a cell’s dry mass.
Answer
Proteins


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Proteins constitute most of a cell’s dry mass.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491549687052

Tags
#biochem #biology #cell
Question
Individual noncovalent bonds are [...] weaker than the typical covalent bonds that create biological molecules
Answer
30–300 times


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Individual noncovalent bonds are 30–300 times weaker than the typical covalent bonds that create biological molecules

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491551784204

Tags
#biochem #biology #cell
Question
Although a protein chain can fold into its correct conformation without out- side help, in a living cell special proteins called [...] often assist in protein folding. Molecular chaperones bind to partly folded polypeptide chains and help them progress along the most energetically favorable folding pathway. In the crowded conditions of the cytoplasm, chaperones are required to prevent the temporarily exposed hydrophobic regions in newly synthesized protein chains from associating with each other to form protein aggregates (see p. 355). However, the final three-dimensional shape of the protein is still specified by its amino acid sequence: chaperones simply make reaching the folded state more reliable.
Answer
molecular chaperones


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Although a protein chain can fold into its correct conformation without out- side help, in a living cell special proteins called molecular chaperones often assist in protein folding. Molecular chaperones bind to partly folded polypeptide chains and help them progress along the most energetically favorable folding pathway. In the c

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
tarvitsemansa palvelu, vaan asiakas on tapaus, joka joutuu alistumaan sääntöjen vaatimaan kohteluun

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
samalle osastolle on yleensä palkattu samantyyppisen koulutuksen saaneita asiantuntijoita työskentelemään samantyyppisten ongelmien parissa. Henkilöstö samastuu osaston päämääriin ja tavoitteisiin jo koulutuksensa puolesta. Osastojen näkökannat loittonevat toisistaan ja koko organisaation tavoitteista. Syntyy ”siiloja”, joiden rajoja on vaikea ylittää

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491560172812

Tags
#biochem #biology #cell
Question
Although a protein chain can fold into its correct conformation without out- side help, in a living cell special proteins called molecular chaperones often assist in protein folding. Molecular chaperones bind to partly folded polypeptide chains and help them progress along the most energetically favorable folding pathway. In the crowded conditions of the cytoplasm, chaperones are required to prevent the temporarily exposed hydrophobic regions in newly synthesized protein chains from associating with each other to form protein aggregates (see p. 355). However, the final three-dimensional shape of the protein is still [...]: chaperones simply make reaching the folded state more reliable.
Answer
specified by its amino acid sequence


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
equired to prevent the temporarily exposed hydrophobic regions in newly synthesized protein chains from associating with each other to form protein aggregates (see p. 355). However, the final three-dimensional shape of the protein is still <span>specified by its amino acid sequence: chaperones simply make reaching the folded state more reliable.<span><body><html>

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491561745676

Tags
#biochem #biology #cell
Question
Although a protein chain can fold into its correct conformation without out- side help, in a living cell special proteins called molecular chaperones often assist in protein folding. Molecular chaperones bind to partly folded polypeptide chains and help them progress along the most energetically favorable folding pathway. In the crowded conditions of the cytoplasm, chaperones are required to prevent the temporarily exposed hydrophobic regions in newly synthesized protein chains from associating with each other to form protein aggregates (see p. 355). However, the final three-dimensional shape of the protein is still specified by its amino acid sequence: chaperones simply make [...]
Answer
reaching the folded state more reliable.


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
s in newly synthesized protein chains from associating with each other to form protein aggregates (see p. 355). However, the final three-dimensional shape of the protein is still specified by its amino acid sequence: chaperones simply make <span>reaching the folded state more reliable.<span><body><html>

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491563318540

Tags
#biochem #biology #cell
Question
Although a protein chain can fold into its correct conformation without out- side help, in a living cell special proteins called molecular chaperones often assist in protein folding. Molecular chaperones [...]. In the crowded conditions of the cytoplasm, chaperones are required to prevent the temporarily exposed hydrophobic regions in newly synthesized protein chains from associating with each other to form protein aggregates (see p. 355). However, the final three-dimensional shape of the protein is still specified by its amino acid sequence: chaperones simply make reaching the folded state more reliable.
Answer
bind to partly folded polypeptide chains and help them progress along the most energetically favorable folding pathway


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Although a protein chain can fold into its correct conformation without out- side help, in a living cell special proteins called molecular chaperones often assist in protein folding. Molecular chaperones bind to partly folded polypeptide chains and help them progress along the most energetically favorable folding pathway. In the crowded conditions of the cytoplasm, chaperones are required to prevent the temporarily exposed hydrophobic regions in newly synthesized protein chains from associating with

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491564891404

Tags
#biochem #biology #cell
Question
Although a protein chain can fold into its correct conformation without out- side help, in a living cell special proteins called molecular chaperones often assist in protein folding. Molecular chaperones bind to partly folded polypeptide chains and help them progress along the most energetically favorable folding pathway. In the crowded conditions of the cytoplasm, chaperones are required to [...] (see p. 355). However, the final three-dimensional shape of the protein is still specified by its amino acid sequence: chaperones simply make reaching the folded state more reliable.
Answer
prevent the temporarily exposed hydrophobic regions in newly synthesized protein chains from associating with each other to form protein aggregates


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
assist in protein folding. Molecular chaperones bind to partly folded polypeptide chains and help them progress along the most energetically favorable folding pathway. In the crowded conditions of the cytoplasm, chaperones are required to <span>prevent the temporarily exposed hydrophobic regions in newly synthesized protein chains from associating with each other to form protein aggregates (see p. 355). However, the final three-dimensional shape of the protein is still specified by its amino acid sequence: chaperones simply make reaching the folded state more reliable.<

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Byrokratian haitat voidaan kiteyttää seuraavasti:
• byrokratia ei mahdollista henkilöstön henkistä kehittymistä ja kasvua
• byrokratia ei ota huomioon sitä, että ihmisten välille syntyy ystävyyssuhteita, jotka aiheuttavat ennalta-arvaamattomuutta mekaanisen, rationaalisen mallin toimintaan
• byrokratia aiheuttaa ongelmia sekä toimintojen väliselle yhteistoiminnalle että johdon ja henkilöstön keskinäisille suhteille
• byrokratian hierarkkinen rakenne vääristää viestintää organisaatiossa ja estää tehokasta tiedonkulkua
• henkilöstön osaaminen ei tule organisaation käyttöön, sillä byrokraattista organisaatiota leimaa tyypillisesti epäluuloisuuden, pelon ja alemmuuden ilmapiiri, ja
• byrokratia muovaa henkilöstön persoonallisuutta niin, että henkilöistä tulee harmaata, ilotonta ja tasapäistä organisaatiolle uskollista joukkoa

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491568037132

Tags
#bayes #programming #r #statistics
Question
The key application that makes Bayes’ rule so useful is when the row variable represents [...] and the column variable represents parameter values.
Answer
data values


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The key application that makes Bayes’ rule so useful is when the row variable represents data values and the column variable represents parameter values.

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Frederick Taylor (1915) kehitti vuosisadan alussa tieteelliseksi liikkeenjohdoksi kutsutun johtamistaidollisen näkökulman

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491571182860

Tags
#bayes #programming #r #statistics
Question
The key application that makes Bayes’ rule so useful is when the row variable represents data values and the column variable represents [...]
Answer
parameter values.


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The key application that makes Bayes’ rule so useful is when the row variable represents data values and the column variable represents parameter values.

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Voitiin kehittää yksi, paras tapa tehdä työ, ja tämä paras tapa voitiin opettaa henkilöstölle

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 1491574328588

Tags
#matlab #programming
Question
As we saw above, use a semi-colon to indicate the end of a row when enter- ing a matrix. Bigger matrices can be constructed from smaller ones, e.g., the statements a=[12;34]; x=[56]; [...] result in a= 12 34 56
Answer
a = [a; x]


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
As we saw above, use a semi-colon to indicate the end of a row when enter- ing a matrix. Bigger matrices can be constructed from smaller ones, e.g., the statements a=[12;34]; x=[56]; a = [a; x] result in a= 12 34 56

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491575901452

Tags
#matlab #programming
Question
if you assign a value to an element with a subscript which is out of range the matrix is [...], for example, the assignment a(3,3) = 7 will add a third column to a with 0s everywhere except at a(3,3)
Answer
enlarged to accommodate the new element


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
if you assign a value to an element with a subscript which is out of range the matrix is enlarged to accommodate the new element, for example, the assignment a(3,3) = 7 will add a third column to a with 0s everywhere except at a(3,3)

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491577474316

Tags
#matlab #programming
Question
the statement [...] results in 789 (returns third row)
Answer
a(3,:)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
the statement a(3,:) results in 789 (returns third row)

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491579047180

Tags
#matlab #programming
Question
[...] results in a= 111 411 789 (replaces the 2-by-2 submatrix composed of the first and second row and the second and third column with a square matrix of 1s)
Answer
a(1:2,2:3) = ones(2)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
a(1:2,2:3) = ones(2) results in a= 111 411 789 (replaces the 2-by-2 submatrix composed of the first and second row and the second and third column with a square matrix of 1s)

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491580620044

Tags
#matlab #programming
Question
a(1:2,2:3) = ones(2) results in a= 111 411 789 ([...])
Answer
replaces the 2-by-2 submatrix composed of the first and second row and the second and third column with a square matrix of 1s


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
a(1:2,2:3) = ones(2) results in a= 111 411 789 (replaces the 2-by-2 submatrix composed of the first and second row and the second and third column with a square matrix of 1s)

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491582192908

Tags
#matlab #programming
Question
Use the [...] to delete entire rows or columns, for example, a(:,2) = [ ] deletes the second column of a
Answer
colon operator and the empty array


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Use the colon operator and the empty array to delete entire rows or columns, for example, a(:,2) = [ ] deletes the second column of a

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491583765772

Tags
#bayes #programming #r #statistics
Question
The factors of Bayes’ rule have specific names that will be used regularly throughout the book, as indicated here: p(θ|D) [...] = p(D|θ) likelihood p(θ) prior / p(D) evidence
Answer
posterior


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The factors of Bayes’ rule have specific names that will be used regularly throughout the book, as indicated here: p(θ|D) posterior = p(D|θ) likelihood p(θ) prior / p(D) evidence

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491585338636

Tags
#bayes #programming #r #statistics
Question
The factors of Bayes’ rule have specific names that will be used regularly throughout the book, as indicated here: p(θ|D) posterior = p(D|θ) [...] p(θ) prior / p(D) evidence
Answer
likelihood


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The factors of Bayes’ rule have specific names that will be used regularly throughout the book, as indicated here: p(θ|D) posterior = p(D|θ) likelihood p(θ) prior / p(D) evidence

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491586911500

Tags
#bayes #programming #r #statistics
Question
The factors of Bayes’ rule have specific names that will be used regularly throughout the book, as indicated here: p(θ|D) posterior = p(D|θ) likelihood p(θ) [...] / p(D) evidence
Answer
prior


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The factors of Bayes’ rule have specific names that will be used regularly throughout the book, as indicated here: p(θ|D) posterior = p(D|θ) likelihood p(θ) prior / p(D) evidence

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491588484364

Tags
#bayes #programming #r #statistics
Question
The factors of Bayes’ rule have specific names that will be used regularly throughout the book, as indicated here: p(θ|D) posterior = p(D|θ) likelihood p(θ) prior / p(D) [...]
Answer
evidence


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The factors of Bayes’ rule have specific names that will be used regularly throughout the book, as indicated here: p(θ|D) posterior = p(D|θ) likelihood p(θ) prior / p(D) evidence

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491590057228

Tags
#bayes #programming #r #statistics
Question
The [...], is the credibility of the θ values without the data D. The “posterior,” p(θ|D),isthe credibility of θ values with the data D taken into account.
Answer
“prior,” p(θ )


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The “prior,” p(θ ), is the credibility of the θ values without the data D. The “posterior,” p(θ|D),isthe credibility of θ values with the data D taken into account.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491591630092

Tags
#bayes #programming #r #statistics
Question
The “prior,” p(θ ), is the [...]. The “posterior,” p(θ|D),isthe credibility of θ values with the data D taken into account.
Answer
credibility of the θ values without the data D


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The “prior,” p(θ ), is the credibility of the θ values without the data D. The “posterior,” p(θ|D),isthe credibility of θ values with the data D taken into account.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491593202956

Tags
#bayes #programming #r #statistics
Question
The “prior,” p(θ ), is the credibility of the θ values without the data D. The [...],isthe credibility of θ values with the data D taken into account.
Answer
“posterior,” p(θ|D)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The “prior,” p(θ ), is the credibility of the θ values without the data D. The “posterior,” p(θ|D),isthe credibility of θ values with the data D taken into account.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491594775820

Tags
#bayes #programming #r #statistics
Question
The “prior,” p(θ ), is the credibility of the θ values without the data D. The “posterior,” p(θ|D),isthe [...]
Answer
credibility of θ values with the data D taken into account.


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The “prior,” p(θ ), is the credibility of the θ values without the data D. The “posterior,” p(θ|D),isthe credibility of θ values with the data D taken into account.

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491596348684

Tags
#bayes #programming #r #statistics
Question
The [...],is the probability that the data could be generated by the model with parameter value θ
Answer
“likelihood,” p(D|θ)


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The “likelihood,” p(D|θ),is the probability that the data could be generated by the model with parameter value θ

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491597921548

Tags
#bayes #programming #r #statistics
Question
The “likelihood,” p(D|θ),is the [...]
Answer
probability that the data could be generated by the model with parameter value θ


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The “likelihood,” p(D|θ),is the probability that the data could be generated by the model with parameter value θ

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491599494412

Tags
#bayes #programming #r #statistics
Question
The term [...] refers specifically to the operation of taking the average of the likelihood, p(D|θ), across all values of θ ,weighted by the prior probability of θ
Answer
“marginal likelihood”


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The term “marginal likelihood” refers specifically to the operation of taking the average of the likelihood, p(D|θ), across all values of θ ,weighted by the prior probability of θ

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 1491601067276

Tags
#bayes #programming #r #statistics
Question
The term “marginal likelihood” refers specifically to the operation of [...]
Answer
taking the average of the likelihood, p(D|θ), across all values of θ ,weighted by the prior probability of θ


statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The term “marginal likelihood” refers specifically to the operation of taking the average of the likelihood, p(D|θ), across all values of θ ,weighted by the prior probability of θ

Original toplevel document (pdf)

cannot see any pdfs







#johtamisajattelu #kehittyminen #markkinointiajattelu
Töiden tieteellinen analyysi voitiin Taylorin mukaan tehdä seuraavasti: 1. Kerätään tietoa siitä, miten todella taitavat ammattimiehet työnsä tekevät. 2. Tutkitaan todella taitavien ammattimiesten työmenetelmiä. 3. Eliminoidaan turhat liikkeet. 4. Kerätään lopuksi yhteen parhaat mahdolliset työmenetelmät

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
tieteellisen analyysin tavoitteena oli Taylorin mukaan saada jokainen työntekijä tekemään yhtä paljon kuin alan ammattitaitoinen työntekijä tekee.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Se johti käytännössä kuitenkin pitkälle vietyyn työnjakoon, työvaiheiden yksinkertaistamiseen ja töiden sisällön köyhtymiseen

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#lk1.1.2 #tieteellinen_liikkeenjohto
johtamis-ja organisointiperiaatteita olivat työntekijöiden tieteellinen valinta, urakkapalkkaus sekä uudenlainen johtamis-ja työn organisointitapa

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Taylorin mielestä ihmistä ohjasivat puhtaasti taloudelliset motiivit

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
suositteli suunnittelun ja toteutuksen täydellistä erottamista toisistaan. Lisäksi hän oli sitä mieltä, että suunnitteluun ja töiden valvontaan erikoistuneet henkilöt voisivat esimiesasemassa toimien osallistua töiden ohjaukseen oman erikoisalansa osalta

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Tayloristisella työn organisoinnilla tarkoitetaankin sitä, että työt on pitkälle ositettu. Kukin työvaihe koostuu vain muutamista harvoista liikkeistä, jotka kuka hyvänsä voi oppia. Työntekijät työskentelevät mekaanisesti pitkiä tuotantosarjoja tehden, ja esimiehet valvovat heidän työskentelyään kontrolloiden, että henkilöstö on paikalla. Urakkajärjestelmät kontrolloivat työn määrää, ja erilliset tarkastajat valvovat työn laatua

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
taylorismi olikin tarkoitettu työnantajille – saamaan työntekijöistä irti entistä enemmän yhä pienemmin kustannuksin

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Henry Fayol (1961) oli hallinnollisen koulukunnan isä

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
käytännön kannalta painottaen tehtävänjaon ja töiden organisoinnin merkitystä. Fayolin mielestä organisaation toiminnot voitiin jakaa kuuteen ryhmään seuraavasti: 1) tekniset, 2) kaupalliset, 3) taloudelliset, 4) turvallisuuteen liittyvät, 5) kirjanpidolliset ja 6) hallinnolliset

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
johtamisen olevan prosessi, josta saatiin parhaiten tietoa tutkimalla sitä, mitä johtaja tekee

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#hallinnollinen_koulukunta #johtamisajattelu #kehittyminen #lk1.1.3 #markkinointiajattelu
virallinen hierarkia tai komentoketju oli organisaation toiminnan itseoikeutettu ja korvaamaton selkäranka. Työntekijöitä oli valvottava ja käskettävä. Kuri oli välttämätön ohjauksen väline

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#ihmissuhdekoulukunta #lk1.2
Mayo päätteli, että tärkeimmät tekijät työelämässä olivat esimiesten ja alaisten hyvät suhteet sekä työryhmän kiinteys

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
edellä mainittujen tekijöiden ohella koehenkilöihin kiinnitetty huomio ja heistä huolehtiminen osaltaan auttoivat parantamaan työtulosta

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
huomio sai koehenkilöt tuntemaan itsensä ylpeiksi siitä, että he kuuluivat koeryhmään. Myös valvonnan puuttuminen ja tutkijoiden sympaattinen suhtautuminen lisäsivät koehenkilöiden myönteistä suhtautumista työhönsä

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Ihmisten välinen vuorovaikutus ja heidän reaktionsa eri tilanteisiin olivat monimutkaisempia kuin

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
oletettiin klassisissa johtamis-ja organisaatioteorioissa, jotka loivat muodollisen johtamis-ja organisaatiojärjestelmän.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#am #am-lk1 #anki #ihmissuhdekoulukunta #johtamis-ja_markkinointiajattelun_kehittyminen
Hawthorne-tutkimuksilla oli käänteentekevä vaikutus organisaatioteorioille, tuotannon sosiologialle ja työn psykologialle. Niiden vaikutuksesta tutkijoiden mielenkiinto siirtyi rakenteiden yksipuolisesta tarkastelusta sosiaalisten järjestelmien tarkasteluun. Tutkimukset johtivat siihen, että organisaatioita alettiin tarkastella sekä sosiaalisina, teknisinä että taloudellisina järjestelminä

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Tarkasteluun tulevat ne ehdot, joiden vallitessa organisaatio voi menestyä ympäristössään. Organisaationäkemyksiin kontingenssiteoria tuli 1960-luvulla, johtamisteorioihin vasta 1970-luvulla. Kontingenssiteoriassa pyrittiin selittämään organisaation ja sen ympäristön välisiä vuorovaikutussuhteita. Kontingenssiteorian mukaan ei ole olemassa yhtä oikeaa organisaatiorakennetta, vaan organisaatiorakenteen tulee sopeutua ympäristön ominaisuuksiin

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Tarkasteluun tulevat ne ehdot, joiden vallitessa organisaatio voi menestyä ympäristössään. Organisaationäkemyksiin kontingenssiteoria tuli 1960-luvulla, johtamisteorioihin vasta 1970-luvulla. Kontingenssiteoriassa pyrittiin selittämään organisaation ja sen ympäristön välisiä vuorovaikutussuhteita. Kontingenssiteorian mukaan ei ole olemassa yhtä oikeaa organisaatiorakennetta, vaan organisaatiorakenteen tulee sopeutua ympäristön ominaisuuksiin

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Hitaasti muuttuvissa ympäristöissä työskentelevillä menestyvillä organisaatioilla oli mekanistinen rakenne. Ne muistuttivat klassisen organisaatioteorian mukaisia organisaatioita

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
nopeasti muuttuvissa ympäristöissä työskentelevissä menestyvissä organisaatioissa oli orgaaninen rakenne

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
pyrittiin korostamaan organisaation kykyä kehittyä ympäristön sille tarjoamien mahdollisuuksien myötä. Samalla vertaus tuo esille organisaation luontaisen yhteyden ja vuorovaikutuksellisen riippuvuussuhteen ympäristönsä kanssa

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Orgaaninen organisaatio puolestaan perustuu joustavaan työnjakoon, avoimeen tiedonkulkuun ja asiantuntemuksen hyväksikäyttöön

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Burns ja Stalker (1961) tekivät ensimmäisen merkittävän tutkimuksen organisaation ja sen ympäristön välisestä vuorovaikutuksest

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Burnsin ja Stalkerin mukaan tehokkain organisoituminen on sellainen, joka on sopusoinnussa ympäristön vaatimuksien kanss

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
TAULUKKO 1. Mekaanisen ja orgaanisen organisaation vertailua

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
organisaatio pyrkii sopeutumaan ympäristön aiheuttamaan epävarmuuteen tiedonvaihdon ja tietojen prosessoinnin avulla

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Dynaamisessa ja nopeasti muuttuvassa ympäristössä tarvitaan nopeaa tiedonkulkua, eri asiantuntijoiden ja eri tasojen välistä jatkuvaa yhteistoimintaa

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
ympäristön muutosnopeus kasvaa, byrokraattinen organisaatio ei kykene hoitamaan tarvittavaa tiedonkulkua eri asiantuntijoiden ja eri tasojen välillä. Epävarmuus ja ennustamattomuus vievät pohjan sääntöihin ja pysyväismääräyksiin perustuvalta organisoitumiselta. Lisäksi hierarkkinen päätöksentekojärjestelmä hidastaa päätösten tekemistä tilanteissa, joissa tarvittaisiin nopeaa toimintaa

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Erilaistuneisuudella he tarkoittivat organisaation osien välisen työnjaon erilaisuutta. Erilaistuneisuus viittaa eri osastojen johdon toiminta-ja suhtautumistapojen erilaisuuteen. Erilaistuneisuus koostuu muun muassa organisaatiorakenteen muodollisuuden asteesta ja ilmapiirin tehtävä- tai ihmiskeskeisyydestä. Integraatiolla Lawrence ja Lorsch tarkoittivat osastojen välisen yhteistyön astetta, osastojen toiminnan koordinaatiota ja osastojen välise yhteistyön laatua

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
menestyvissä yrityksissä sekä organisaation erilaistumisen aste että integraation aste olivat sopusoinnussa ympäristön vaatimusten kanssa. Dynaamisessa ympäristössä organisaatioiden tulisi kyetä olemaan samanaikaisesti sekä erilaistuneita että integroituneita

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Dynaamisessa ympäristössä työskentelevät menestyvät organisaatiot olivat kyenneet erilaistamaan toimintonsa, ja niiden johto kykeni myös ratkaisemaan esiintyvät ristiriidat kaikkia osapuolia hyödyttävällä tavalla

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Menestyvissä yrityksissä johtoon kuuluvat luottivat siihen, että heitä arvioidaan ja palkitaan pikemminkin kokonaistuloksen kehittymisen kuin heidän oman osastonsa tuloksen perusteella

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Vakaassa ympäristössä menestyvillä yrityksillä sen sijaan oli alhainen erilaistuneisuuden aste. Niissä integraatiosta huolehdittiin sääntöjen, pysyväismääräysten ja toimintaohjeiden avulla. Melko dynaamisessa ympäristössä menestyvissä yrityksissä oli kohtalainen erilaistuneisuuden aste ja integraatiosta huolehdittiin osin johtamisjärjestelmän ja osin sääntöjen avulla

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Tutkimuksen tulokset voidaan kiteyttää seuraavasti: • Dynaamisessa ympäristössä työskentelevän organisaation tulisi olla joustava ja avoin. Johtamisen tulisi perustua yhteisiin päämääriin sekä arvoihin sitoutumisen ja yhtenäisen kulttuurin luomisen varaan. • Vakaassa ympäristössä työskentelevä organisaatio voi olla hierarkkinen ja jäykkä. Johtamisjärjestelmä voi olla sääntöjen ja pysyväismääräyksien noudattamista valvova

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Mintzberg (1979) jaotteli organisaatioita sen mukaan, mihin ympäristöihin ja mihin tilanteisiin ne sopivat. Ympäristön olosuhteita tarkastellessaan Mintzberg erottaa toisistaan ympäristön stabiiliuden ja monimutkaisuuden

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Mintzbergin mukaan yksinkertaiseen ja vakaaseen ympäristöön soveltuu mekanistinen byrokratia, vakaaseen ja monimutkaiseen ympäristöön puolestaan asiantuntijabyrokratia. Dynaamiseen ja yksinkertaiseen ympäristöön soveltuu Mintzbergin mukaan yksinkertainen rakenne, monimutkaiseen ja dynaamiseen ympäristöön taas orgaaninen, joustava tai avoin organisaatiorakenne (adhocratia)

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
yksinkertainen rakenne, on useimmiten nuori, vastaperustettu, pienehkö organisaatio, joka toimii dynaamisessa ympäristössä, avoimen kilpailun tilanteessa

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
organisaatio rakentuu yrittäjän varaan. Se on joustava. Organisoituminen ei perustu muodollisille säännöille tai johtamisjärjestelmille. Tällaisessa organisaatiossa ei ole esikuntia eikä suunnittelujärjestelmiä

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Yksinkertainen rakenne sopii dynaamiseen ja vakaaseen ympäristöön sekä avoimeen kilpailutilanteeseen

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Mintzberg jakaa byrokratiat mekaanisiin byrokratioihin ja asiantuntijabyrokratioihin. Esimerkkinä mekaanisesta byrokratiasta ovat teollisuuslaitosten organisaatiot, ja asiantuntijabyrokratiasta esimerkkeinä ovat yliopistojen, koulujen ja sairaaloiden organisaatiot

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Mekaaninen byrokratia on Mintzbergin terminologiassa lähinnä Weberin byrokratian ihannemallia. Sille on tyypillistä sääntöjen runsaus, toimintojen korkea ennustettavuus, korkea standardoinnin aste, pitkälle ositettu työnjako, hierarkkinen rakenne ja muodollinen kommunikaatio

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Mekaanisille byrokratioille on tyypillistä korkea rationalisoinnin aste. Suorittavan tason tehtävät ovat yksinkertaisia, eikä niiden hoitajilta edellytetä korkeaa koulutusta. Pitkälle ositetut tehtävät ja korkea standardoinnin aste edellyttävät tehokasta hallintoa. Keskijohdon asema mekaanisessa byrokratiassa on korostunut. Keskijohdon tehtävänä on selvittää työntekijöiden ja johdon välisiä erimielisyyksiä ja toimia oman alueensa asiantuntijana

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Mekaaninen byrokratia soveltuu yksinkertaiseen ja vakaaseen ympäristöön

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Vakaisiin mutta monimutkaisiin ympäristöihin soveltuu asiantuntijabyrokratia. Siinä toimintoja hoitavat pitkälle koulutetut asiantuntijat, joilla on oman alansa ammattitaito ja professionaaliset arvot

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
taidot ja koulutuksen myötä omaksutut arvot takaavat myös yhteistyön sujumisen

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Tulosyksikköorganisaatio ei oikeastaan ole yksi yhtenäinen organisaatio vaan useasta erillisestä organisaatiosta koostunut organisaatiorakennelma. Tulosyksikköorganisaatio soveltuu erilaistuneille markkinoille. Tulosyksikkömalli toimii parhaiten, kun eri tulosyksiköt rakentuvat organisaatioiltaan mekaanisista byrokratioista, jotka työskentelevät eri toimialoilla tai eri markkina-alueilla

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Tulosyksikkömallilla on Mintzbergin mukaan neljä vahvuutta: • Se mahdollistaa pääomien tehokkaan käytön. Keskushallinto voi sijoittaa rahaa yksikköihin, joiden markkinatilanne on parhaiten kehittyvä. • Se mahdollistaa keskijohdon kehittymisen yleisjohdollisiin tehtäviin. • Se jakaa riskin useiden tulosyksiköiden kesken. • Sekä strateginen että operatiivinen johto voivat tulosyksikkömallissa optimoida omat toimenpiteensä

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Mintzbergin jaottelussa viides organisaatiotyyppi on adhokratia, jota voidaan kutsua myös joustavaksi organisaatioksi. Ympäristössä, joka on sekä dynaaminen että monimutkainen, organisaation tulisi olla orgaaninen ja epämuodollinen, ryhmätyöskentelylle ja ihmisten asiantuntemukselle perustuva

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Adhokratia onkin organisaatio, jonka rakenne syntyy aina uudelleen kulloistenkin tarpeiden mukaan ja jäsentyy esimerkiksi hankkeiksi. Projektiorganisaatio on esimerkki adhokratiasta

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Adhokratiassa useimmat tehtävät tehdään projektiryhmissä tai muissa tilapäisissä ryhmissä. Johtaminen perustuu pikemminkin ihmisten sitoutumiseen tehtäviinsä ja organisaation päämääriin kuin valvontaan tai käskytykseen. Sitoutuminen toteutuu osallistuvan johtamistavan sekä itsenäisten, haasteellisten ja vaihtelevien tehtävien kautta

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Adhokratian organisaatiorakenne on matala. Päätökset tehdään niin lähellä tiedon lähdettä kuin mahdollista nopean palautteen ja organisaation toiminnan joustavuuden aikaansaamiseksi

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Adhokratiassa suunnittelu, toteutus ja valvonta ovat sen asiantuntijan vastuulla, joka työn tekee

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
Organisaatiotutkimukseen kulttuurinäkökulma tuli kuitenkin verraten myöhään, pääosin vasta 1980-luvulla

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Kulttuuriviitekehyksen käytön yleistyminen liittyi pyrkimykseen hakea organisaation menestymiseen liittyviä entistä syvällisempiä selitysmalleja

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Organisaatiokulttuurilla voidaan tarkoittaa muun muassa niitä tapoja, joita organisaation palveluksessa olevilla ihmisillä on. Tällöin kulttuuri määrittyy ”tavaksemme toimia”. Kulttuurilla voidaan tarkoittaa myös niitä ajattelutapoja ja selitysmalleja, joiden kautta ihmiset pyrkivät tulkitsemaan ja ymmärtämään ympärillään olevia tapahtumia. Tällöin kulttuurinäkemyksessä painottuvat esimerkiksi symbolit ja arvot

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Mikäli kulttuuria pidetään tapana toimia, se voidaan määritellä yleisesti omaksutuksi tavaksi havaita, uskoa, arvioida ja käyttäytyä

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
organisaatiot voidaan tiettyyn rajaan asti ymmärtää jaetuiksi merkitysjärjestelmiksi, jotka ovat sekä kulttuurin tuotteita että kulttuurin tuottajia, joiden ydin perustuu kulttuurin arvoihin. (Smircich 1985.)

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Mikäli kulttuuria pidetään symbolijärjestelmänä, se voidaan määritellä yhteisesti omaksutuksi symbolijärjestelmäksi, jonka kautta ihmiset tulkitsevat kokemuksiaan ja ohjaavat toimintojaan. (Allaire & Firsirotu 1984.)

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Kulttuuriviitekehyksen käyttöä vaikeuttaa erilaisten koulukuntien olemassaolon lisäksi se, että kulttuurilla on useita syvyystasoja. Organisaatiokulttuuria onkin pidettävä syvällisenä ja monimutkaisena ilmiönä. Muun muassa Schein (1985) on esittänyt ajatuksen kulttuurin syvärakenteesta. Hän määrittelee kulttuurin joukoksi perusoletuksia, jotka tietty ihmisyhteisö on luonut oppiessaan sopeutumaan ympäristöönsä ja pyrkiessään säilyttämään sisäisen kiinteytensä. Perusoletukset ovat ajan myötä osoittautuneet melko toimiviksi ratkaisuiksi, ja siksi ne on opetettu uusille jäsenille oikeana tapana havaita, ajatella ja tuntea

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Kulttuurin synty puolestaan liittyy seuraavanlaisiin oletuksiin: 1. On olemassa tietty määrä ongelmia, joihin jokaisen yhteisön on löydettävä ratkaisu. 2. On olemassa tietty määrä vaihtoehtoja, joiden avulla näihin ongelmiin voidaan vastata. 3. Kaikki vaihtoehdot ovat avoinna jokaiselle yhteisölle, mutta eri aikoina eri yhteisöt ovat painottaneet ratkaisuja eri tavoin. 4. Jokaisella yhteisöllä on tietty vaihtoehtojen preferenssijärjestys. (Adler & Jelinek 1986.)

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
kulttuurin määrittelyssä tulee erottaa tarkastelussa toisistaan aiemmin kuvattu niin sanottu syvä perusoletusten taso, näkyvä artefaktien taso ja kulttuuriset arvot

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
organisaation suhdetta ympäristöön, todellisuuteen, aikaan ja tilaan, samoin organisaation suhdetta ihmisiin toimijoina

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Niillä tarkoitetaan esinettä tai asiaa, joka ilmentää kulttuuria. Niillä voidaan viitata myös organisaation näkyvään käyttäytymiseen, esimerkiksi puhuttuun kieleen, mukaan lukien slangi, huumori ja vastaavat. Artefakteja ovat myös organisaation tuotteet, fyysinen ympäristö, visuaalinen materiaali ja niin edelleen

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Organisaatiokulttuuri on läsnä kaikessa organisaation toiminnassa, mutta erityisesti se

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#anki #johtamisajattelu #kehittyminen #markkinointiajattelu
ilmenee muun muassa • myyteissä • tarinoissa • kielessä ja puheessa • vertauskuvissa • symboleissa • juhlissa • riiteissä • rituaaleissa • arvojärjestelmissä ja käyttäytymisnormeissa. (Shrivastava 1985)

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
myytti kuvaavat yhteisön jäsenten yhteisiä näkemyksiä siitä, kuinka yhteisö on selvinnyt kriittisistä tilanteista

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Organisaation tarinoissa kuvataan usein jotakin tapahtumaa

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Symbolit auttavat organisaation jäseniä samastumaan organisaatioon ja sitoutumaan sen päämääriin

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Juhla on useiden riittien järjestelmä, joka ilmenee yhdessä tapahtumassa, joka usein saa ylevän muodon

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Rituaali on standardoitu, yksityiskohtainen käyttäytymistapa, jonka kautta pyritään hallitsemaan ahdistuneisuutta. Perinteinen esimerkki rituaaleista on kokous. Kokouksen merkitys ei ole yksinomaan siinä, että sen avulla saadaan oikeutus päätökselle, vaan myös siinä, että se vahvistaa tiettyä valtarakennetta

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Symbolisen toiminnan, kuten myyttien, riittien ja tarinoiden, merkitys on siinä, että se saattaa tarjota hyväksyttävän ratkaisun tilanteissa, joissa todellista ratkaisua ei ole saatavissa. Näin nämä toiminnat luovat tuttuuden tunnetta, vähentävät jännitteitä ja luovat turvallisuuden ja jatkuvuuden tunnetta

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Organisaatiokulttuuri liittyy muun muassa seuraaviin seikkoihin, joiden avulla organisaatio pyrkii selviytymään ja menestymään ympäristössään:
• Yhteisnäkemys organisaation päämääristä ja tavoitteista. Jokaisen yhteisön on kyettävä luomaan tietty yleisesti omaksuttu näkemys päämääristään ja tavoitteistaan sekä olemassaolonsa oikeutuksesta.
• Yhteisnäkemys käytettävistä keinoista. Päämääriä ei voi saavuttaa, mikäli ei vallitse riittävän yhtenevää näkemystä keinoista, joilla päämääriin pyritään.
Yhteisnäkemys niistä kriteereistä, joiden avulla tuloksia mitataan. Yhteisössä täytyy vallita yhteisymmärrys suoritusten ja tulosten arvioinnista, jotta toimintaa voidaan korjata riittävästi.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#johtamisajattelu #kehittyminen #markkinointiajattelu
Organisaatiokulttuuri liittyy seuraaviin näkemyksiin, joiden avulla pyritään säilyttämään yhteisön kiinteys: • yhteinen kieli ja yhteinen käsitejärjestelmä • yhteinen näkemys työnjaosta ja vallankäytöstä • yhteinen näkemys suhteista työtovereihin • yhteinen näkemys palkkioiden ja rangaistusten jaon perusteista

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs