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Annotation 10564537612

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.

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Annotation 1482139503884

#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.

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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.

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Annotation 1482207137036

#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: \(H (P, Q ) = − E _{x \sim P} \space log Q(x)\)

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Annotation 1482241740044

#deeplearning #neuralnetworks

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 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

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Annotation 1482249604364

#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)

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Flashcard 1489898704140

Question

what is the name of your mother

Answer

ChaCha

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Annotation 1490770857228

#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.

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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)

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

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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

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

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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

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

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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

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

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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

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

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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

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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

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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

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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

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Flashcard 1490906647820

Tags

#matlab #programming

Question

The function [...] draws little arrows to indicate a gradient or other vec- tor field

Answer

quiver

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

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Flashcard 1490908220684

Tags

#matlab #programming

Question

The function quiver [...]

Answer

draws little arrows to indicate a gradient or other vec- tor field

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

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Flashcard 1490909793548

Tags

#matlab #programming

Question

The [...] function can also be used to ‘visualize’ a matrix

Answer

mesh

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

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Flashcard 1490911366412

Tags

#matlab #programming

Question

The mesh function can also be used to [...]

Answer

‘visualize’ a matrix

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

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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

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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

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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

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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

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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

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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

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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

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

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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.

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

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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

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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

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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

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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

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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.

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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

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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.

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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

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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.

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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.

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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.

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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

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

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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

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

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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

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

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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

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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

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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

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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

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

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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

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

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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

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

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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

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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

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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

\(D_{KL}( P||Q) = E_{x\sim P} [log\frac{ P (x )}{ Q (x )}] = E_{x \sim P} [log P(x) - log Q(x)] \)

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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

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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.

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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

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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

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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

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[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

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[...] 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

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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

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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

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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

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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

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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

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Annotation 1491377720588

#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,

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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

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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

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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

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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

\(H ( P , Q ) = H ( P ) + D_{KL}( P|| Q )\)

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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: \(H (P, Q ) = − E _{x \sim P} \space log Q(x)\)

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Flashcard 1491394759948

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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

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There are t w o main kinds of structured probabilistic mo dels: [...]

Answer

directed and undirected.

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

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There are t w o main kinds of [...]: directed and undirected.

Answer

structured probabilistic mo dels

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

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[...] mo dels use graphs with directed edges, and they represen t fac- torizations into conditional probability distributions

Answer

Directed

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

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Directed mo dels use [...], and they represen t fac- torizations into conditional probability distributions

Answer

graphs with directed edges

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

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Directed mo dels use graphs with directed edges, and they represen t [...]

Answer

fac- torizations into conditional probability distributions

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

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[...] mo dels use graphs with undirected edges, and they represen t factorizations into a set of functions

Answer

Undirected

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

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Undirected mo dels use [...] and they represen t factorizations into a set of functions

Answer

graphs with undirected edges,

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

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Undirected mo dels use graphs with undirected edges, and they represen t [...]

Answer

factorizations into a set of functions

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

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Any set of no des that are all connected to each other in G is called a [...]

Answer

clique.

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

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Any [...] is called a clique.

Answer

set of no des that are all connected to each other in G

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

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Flashcard 1491417042188

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[...] o ccurs when n um b ers near zero are rounded to zero

Answer

Underﬂo w

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

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Underﬂo w o ccurs when [...]

Answer

n um b ers near zero are rounded to zero

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

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[...] o ccurs when n um bers with large magnitude are appro ximated as ∞ or −∞

Answer

Overﬂo w

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

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Overﬂo w o ccurs when [...]

Answer

n um bers with large magnitude are appro ximated as ∞ or −∞

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

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Annotation 1491425430796

#johtamisajattelu #kehittyminen #markkinointiajattelu

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

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Both of these diﬃculties can b e resolved by instead [...] 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 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 )

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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

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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 − max_i x_i .

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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 [...], 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

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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 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

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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 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

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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 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<body></h

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Flashcard 1491437751564

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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 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

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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, 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<body><html>

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Flashcard 1491439586572

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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 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

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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 underﬂow in the denominator leading to a division by zero<body><html>

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Flashcard 1491441159436

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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 )

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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

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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)

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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

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Flashcard 1491445878028

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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

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

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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

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

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Flashcard 1491449023756

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#deeplearning #neuralnetworks

Question

A lo cal maximum is a p oint where [...]

Answer

f ( x ) is higher than at all neighboring p oin ts

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

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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

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

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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

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

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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

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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

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Flashcard 1491456888076

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#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

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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 )

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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 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<body><html>

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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

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• 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<body><html>

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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

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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 accept or reject the proposal according to p move<body><html>

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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

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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 )

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Flashcard 1491466063116

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#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 )

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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

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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

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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

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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

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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

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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

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Flashcard 1491476811020

Tags

#matlab #programming

Question

The command [...] lists all the possible property values (where appro- priate)

Answer

set(handle)

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

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Flashcard 1491478383884

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#matlab #programming

Question

The command set(handle) [...]

Answer

lists all the possible property values (where appro- priate)

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

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Annotation 1491480743180

#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.

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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

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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

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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

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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

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Annotation 1491485986060

#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.

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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

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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)

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Annotation 1491489918220

#1 #2 #anki #byrokratian_ihannemalli #klassiset_koulukunnat #lk1

tehokkuus aiheutui siitä, että virkoihin valittiin niihin koulutuksen saaneita henkilöitä

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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)

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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

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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

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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

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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.

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

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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

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

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Flashcard 1491502763276

Tags

#deeplearning #neuralnetworks

Question

F rom this, we can see that a [...] is its own transp ose: a a =

Answer

scalar

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

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Flashcard 1491504860428

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#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

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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

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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

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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

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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

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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

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Annotation 1491514821900

#johtamisajattelu #kehittyminen #markkinointiajattelu

byrokratian tehokkuus perustui persoonattomille säännöstöille, joiden toimeenpanoa koulutettu henkilöstö toteutti

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Flashcard 1491517181196

Tags

#deeplearning #neuralnetworks

Question

Is matrix multiplication is distributive?

Answer

Yes

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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

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Annotation 1491518491916

#johtamisajattelu #kehittyminen #markkinointiajattelu

Byrokratian hyvinä puolina voidaan pitää toimintojen rationaalisuutta ja ennustettavuutta.

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Flashcard 1491522686220

Tags

#deeplearning #neuralnetworks

Question

Is matrix multiplication associative?

Answer

It is also asso ciativ e

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Flashcard 1491527404812

Tags

#deeplearning #neuralnetworks

Question

Is matrix multiplication commutative?

Answer

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Annotation 1491529239820

#johtamisajattelu #kehittyminen #markkinointiajattelu

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

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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)

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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)

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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

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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 phosphofructokinase<body><html>

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Annotation 1491539201292

#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

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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

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Question

[...] constitute most of a cell’s dry mass.

Answer

Proteins

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Proteins constitute most of a cell’s dry mass.

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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

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

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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

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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

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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 specified by its amino acid sequence: chaperones simply make reaching the folded state more reliable.<body><html>

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Flashcard 1491561745676

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#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.

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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 reaching the folded state more reliable.<body><html>

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Flashcard 1491563318540

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#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

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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

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Flashcard 1491564891404

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#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

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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.<

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Annotation 1491566464268

#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

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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

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

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Annotation 1491569609996

#johtamisajattelu #kehittyminen #markkinointiajattelu

Frederick Taylor (1915) kehitti vuosisadan alussa tieteelliseksi liikkeenjohdoksi kutsutun johtamistaidollisen näkökulman

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Flashcard 1491571182860

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#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.

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

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Annotation 1491572755724

#johtamisajattelu #kehittyminen #markkinointiajattelu

Voitiin kehittää yksi, paras tapa tehdä työ, ja tämä paras tapa voitiin opettaa henkilöstölle

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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]

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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

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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

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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)

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Flashcard 1491577474316

Tags

#matlab #programming

Question

the statement [...] results in 789 (returns third row)

Answer

a(3,:)

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

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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)

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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)

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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

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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)

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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

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

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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

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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

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Flashcard 1491585338636

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#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

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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

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Flashcard 1491586911500

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#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

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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

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Flashcard 1491588484364

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#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

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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

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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 )

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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.

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Flashcard 1491591630092

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#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

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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.

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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)

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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.

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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.

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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.

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Flashcard 1491596348684

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#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)

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

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Flashcard 1491597921548

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#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

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

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Flashcard 1491599494412

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#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”

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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

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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

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Annotation 1491637767436

#johtamisajattelu #kehittyminen #markkinointiajattelu

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Annotation 1491639340300

#johtamisajattelu #kehittyminen #markkinointiajattelu

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

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Annotation 1491640913164

#johtamisajattelu #kehittyminen #markkinointiajattelu

nopeasti muuttuvissa ympäristöissä työskentelevissä menestyvissä organisaatioissa oli orgaaninen rakenne

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Annotation 1491642486028

#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

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Annotation 1491644058892

#johtamisajattelu #kehittyminen #markkinointiajattelu

Orgaaninen organisaatio puolestaan perustuu joustavaan työnjakoon, avoimeen tiedonkulkuun ja asiantuntemuksen hyväksikäyttöön

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Annotation 1491645631756

#johtamisajattelu #kehittyminen #markkinointiajattelu

Burns ja Stalker (1961) tekivät ensimmäisen merkittävän tutkimuksen organisaation ja sen ympäristön välisestä vuorovaikutuksest

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Annotation 1491647204620

#johtamisajattelu #kehittyminen #markkinointiajattelu

Burnsin ja Stalkerin mukaan tehokkain organisoituminen on sellainen, joka on sopusoinnussa ympäristön vaatimuksien kanss

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Annotation 1491648777484

#johtamisajattelu #kehittyminen #markkinointiajattelu

TAULUKKO 1. Mekaanisen ja orgaanisen organisaation vertailua

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Annotation 1491650350348

#anki #johtamisajattelu #kehittyminen #markkinointiajattelu

organisaatio pyrkii sopeutumaan ympäristön aiheuttamaan epävarmuuteen tiedonvaihdon ja tietojen prosessoinnin avulla

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Annotation 1491651923212

#johtamisajattelu #kehittyminen #markkinointiajattelu

Dynaamisessa ja nopeasti muuttuvassa ympäristössä tarvitaan nopeaa tiedonkulkua, eri asiantuntijoiden ja eri tasojen välistä jatkuvaa yhteistoimintaa

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Annotation 1491653496076

#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

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Annotation 1491655068940

#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

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Annotation 1491658214668

#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

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Annotation 1491661884684

#johtamisajattelu #kehittyminen #markkinointiajattelu

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

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Annotation 1491663457548

#johtamisajattelu #kehittyminen #markkinointiajattelu

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

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Annotation 1491665030412

#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

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Annotation 1491666603276

#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

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Annotation 1491668176140

#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

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Annotation 1491669749004

#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)

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Annotation 1491671321868

#anki #johtamisajattelu #kehittyminen #markkinointiajattelu

yksinkertainen rakenne, on useimmiten nuori, vastaperustettu, pienehkö organisaatio, joka toimii dynaamisessa ympäristössä, avoimen kilpailun tilanteessa

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Annotation 1491672894732

#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

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Annotation 1491674467596

#anki #johtamisajattelu #kehittyminen #markkinointiajattelu

Yksinkertainen rakenne sopii dynaamiseen ja vakaaseen ympäristöön sekä avoimeen kilpailutilanteeseen

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Annotation 1491676040460

#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

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Annotation 1491677613324

#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

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Annotation 1491679186188

#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

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Annotation 1491680759052

#johtamisajattelu #kehittyminen #markkinointiajattelu

Mekaaninen byrokratia soveltuu yksinkertaiseen ja vakaaseen ympäristöön

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Annotation 1491686264076

#johtamisajattelu #kehittyminen #markkinointiajattelu

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

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Annotation 1491687836940

#johtamisajattelu #kehittyminen #markkinointiajattelu

taidot ja koulutuksen myötä omaksutut arvot takaavat myös yhteistyön sujumisen

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Annotation 1491691506956

#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

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Annotation 1491693079820

#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

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Annotation 1491694652684

#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

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Annotation 1491696225548

#anki #johtamisajattelu #kehittyminen #markkinointiajattelu

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

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Annotation 1491697798412

#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

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Annotation 1491699371276

#anki #johtamisajattelu #kehittyminen #markkinointiajattelu

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

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Annotation 1491700944140

#johtamisajattelu #kehittyminen #markkinointiajattelu

Adhokratiassa suunnittelu, toteutus ja valvonta ovat sen asiantuntijan vastuulla, joka työn tekee

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Annotation 1491702517004

#anki #johtamisajattelu #kehittyminen #markkinointiajattelu

Organisaatiotutkimukseen kulttuurinäkökulma tuli kuitenkin verraten myöhään, pääosin vasta 1980-luvulla

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Annotation 1491704352012

#johtamisajattelu #kehittyminen #markkinointiajattelu

Kulttuuriviitekehyksen käytön yleistyminen liittyi pyrkimykseen hakea organisaation menestymiseen liittyviä entistä syvällisempiä selitysmalleja

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Annotation 1491705924876

#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

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Annotation 1491707759884

#johtamisajattelu #kehittyminen #markkinointiajattelu

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

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Annotation 1491709332748

#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.)

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Annotation 1491710905612

#johtamisajattelu #kehittyminen #markkinointiajattelu

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

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Annotation 1491712740620

#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

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Annotation 1491714313484

#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.)

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Annotation 1491716148492

#johtamisajattelu #kehittyminen #markkinointiajattelu

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

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Annotation 1491717721356

#johtamisajattelu #kehittyminen #markkinointiajattelu

organisaation suhdetta ympäristöön, todellisuuteen, aikaan ja tilaan, samoin organisaation suhdetta ihmisiin toimijoina

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Annotation 1491719294220

#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

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Annotation 1491721129228

#johtamisajattelu #kehittyminen #markkinointiajattelu

Organisaatiokulttuuri on läsnä kaikessa organisaation toiminnassa, mutta erityisesti se

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Annotation 1491722702092

#anki #johtamisajattelu #kehittyminen #markkinointiajattelu

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

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Annotation 1491724537100

#johtamisajattelu #kehittyminen #markkinointiajattelu

myytti kuvaavat yhteisön jäsenten yhteisiä näkemyksiä siitä, kuinka yhteisö on selvinnyt kriittisistä tilanteista

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Annotation 1491726109964

#johtamisajattelu #kehittyminen #markkinointiajattelu

Organisaation tarinoissa kuvataan usein jotakin tapahtumaa

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Annotation 1491727682828

#johtamisajattelu #kehittyminen #markkinointiajattelu

Symbolit auttavat organisaation jäseniä samastumaan organisaatioon ja sitoutumaan sen päämääriin

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Annotation 1491729255692

#johtamisajattelu #kehittyminen #markkinointiajattelu

Juhla on useiden riittien järjestelmä, joka ilmenee yhdessä tapahtumassa, joka usein saa ylevän muodon

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Annotation 1491730828556

#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

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Annotation 1491732401420

#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

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Annotation 1491733974284

#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.

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Annotation 1491736333580

#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

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Edited, memorised or added to reading queue

on 19-Mar-2017 (Sun)

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Parent (intermediate) annotation

Original toplevel document (pdf)

Parent (intermediate) annotation

Original toplevel document (pdf)

Parent (intermediate) annotation

Original toplevel document (pdf)

Parent (intermediate) annotation

Original toplevel document (pdf)

Parent (intermediate) annotation

Original toplevel document (pdf)

Parent (intermediate) annotation

Original toplevel document (pdf)

Parent (intermediate) annotation

Original toplevel document (pdf)

Parent (intermediate) annotation

Original toplevel document (pdf)

Parent (intermediate) annotation

Original toplevel document (pdf)

Parent (intermediate) annotation

Original toplevel document (pdf)

Parent (intermediate) annotation

Original toplevel document (pdf)

pdf

pdf

pdf