on 06-Mar-2017 (Mon)

Flashcard 1425555721484

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Question

Reading 13 explains the concepts and tools of demand and supply analysis—the study of [...] to determine transaction prices and quantities.

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Reading 13 explains the concepts and tools of demand and supply analysis—the study of how buyers and sellers interact to determine transaction prices and quantities.

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Study Session 4
This study session focuses on the microeconomic principles used to describe the marketplace behavior of consumers and firms. Reading 13 explains the concepts and tools of demand and supply analysis—the study of how buyers and sellers interact to determine transaction prices and quantities. Reading 14 covers the theory of the consumer, which addresses the demand for goods and services by individuals who make decisions to maximize the satisfaction they receive fr

Flashcard 1429089422604

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#sister-miriam-joseph #trivium
Question
A learner must use [...] to join the facts together to form a significant whole. This makes learning easier, more interesting, and much more valuable.
mental hooks and eyes

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A learner must use mental hooks and eyes to join the facts together to form a significant whole. This makes learning easier, more interesting, and much more valuable.

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Flashcard 1429120093452

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#sister-miriam-joseph #trivium
Question
[...] is right reason about something to be done
Prudence

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Prudence is right reason about something to be done

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Flashcard 1430795193612

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#cfa #cfa-level-1 #economics #microeconomics #reading-14-demand-and-supply-analysis-consumer-demand #section-3-utility-theory #study-session-4
Question
A distinctly different consumption bundle would contain all of the same goods but [...], again allowing for the possibility of a [...] of one or more of the goods.
in different quantities

zero quantity

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A distinctly different consumption bundle would contain all of the same goods but in different quantities, again allowing for the possibility of a zero quantity of one or more of the goods.

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3. UTILITY THEORY: MODELING PREFERENCES AND TASTES
mer might enjoy consuming. Each of those goods and services can be represented in a given basket by a non-negative quantity, respectively, of all the possible goods and services. Any given basket could have zero of one or more of those goods. <span>A distinctly different consumption bundle would contain all of the same goods but in different quantities, again allowing for the possibility of a zero quantity of one or more of the goods. For example, bundle Amight have the same amount of all but one of the goods and services as bundle B but a different amount of that one. Bundles A and B would be considered two distinct

Flashcard 1432405019916

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#conjunction #italian #italian-grammar
Question
i giudici dicono che bisogna cambiare la legge
the judges say that the law should be changed

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uses within a sentence: Marco e Davide ‘Marco and Davide’; con amore ma con disciplina ‘with love but with discipline’; sono andata a letto perché ero stanca ‘I went to bed because I was tired’; i giudici dicono che bisogna cambiare la legge ‘<span>the judges say that the law should be changed’. Conjunctions can either be coordinating, linking two phrases or clauses of equal weight, or subordinating, linking a main clause and subordinate clause.<span><body><html>

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Flashcard 1432441195788

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#sister-miriam-joseph #trivium
Question
Humans are [...] and therefore have someone to say things to.
social

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humans alone among animals have the power to think. Consequently, they alone have language in the proper sense of the word. 1 This follows from their nature, for they are rational and therefore have something to say, social and therefore have someone to say it to, and animal and therefore require a physical mode of communicating ideas from one mind, which

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Flashcard 1432851451148

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#i-q #types-of-inteligence
Question
the [...] is the only area of the brain where an increase in blood flow takes place when volunteers tackle complicated puzzles.
lateral prefrontal cortex

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the lateral prefrontal cortex istheonlyarea of the brain where an increase in blood flow takes place when volunteers tackle complicated puzzles.

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Flashcard 1442133183756

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#build-in-sleep #six-tips-for-working-with-the-brain
Question
This blended approach [...] allows me to create three retrievals spaced with sleep, and it also starts to build the habits of the behaviors I am trying to cultivate.
(Pre-learn, teaching -or getting tought- and TED talks and stuff afterwards)

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This blended approach (Pre-learn, teaching -or getting tought- and TED talks and stuff afterwards) allows me to create three retrievals spaced with sleep, and it also starts to build the habits of the behaviors I am trying to cultivate.

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Unknown title
benefit seems to be better at three, so I focus on that number of retrievals in my own learning design. You can certainly build three retrievals into one learning event, but retention will be even more powerful if you add sleep to the mix. <span>Tip #5: Build in sleep between learning It turns out that the sleeping brain plays a large role in how long-term memories are formed. While we sleep, the brain pushes information that we learned that day from our short-term memory into our long-term memory. It's when we sleep that our brain adds the day's learning onto existing schemas, and physically builds and strengthens neural pathways. It also does a little housecleaning. Every day, we take in thousands of bits of information and it is during sleep that our brain chooses which of those bits is worthy of being retained. It even revisits items already in long-term memory and deletes the information that has not been activated in a while. The animated movie Inside Out does a great job of depicting this process. While Riley is sleeping, the minion-like workers in her brain decide to vacuum out most of the names of the U.S. presidents. So how can we use sleep to enhance our learning events? Flip the classroom and use blended learning. I now have learners do some pre-learning a few days prior, then we take a deeper dive in the classroom through hands-on application. I extend their learning with post-event opportunities and resources. For example, when I design leadership training, learners are asked to watch a corresponding online course at lynda.com. They can do this at their own pace and it frees me up from teaching some of that content so that I can use our in-person time for more focused work. When we come together, we do in-depth hands-on practice of the skills I want them to use. And after the event, I provide them with additional learning materials such as links to TED Talks, articles, and assignments to further hone their skills. This blended approach allows me to create three retrievals spaced with sleep, and it also starts to build the habits of the behaviors I am trying to cultivate. Tip #6: Be a habit designer Ultimately, the goal of most learning activities is behavior change. No matter the topic, we are trying to elicit new and better behaviors in the learner

Flashcard 1450669116684

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Question
the minimum point on the AVC does not correspond to the [...] for [...]
least-cost quantity

average total cost.

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the minimum point on the AVC does not correspond to the least-cost quantity for average total cost.

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Average variable cost (AVC) is derived by dividing total variable cost by quantity. For example, average variable cost at 5 units is (300 ÷ 5) or 60. Over an initial range of production, average variable cost declines and then reaches a minimum point. Thereafter, cost increases as the firm utilizes more of its production capacity. This higher cost results primarily from production constraints imposed by the fixed assets at higher volume levels. The minimum point on the AVC coincides with the lowest average variable cost. However, the minimum point on the AVC does not correspond to the least-cost quantity for average total cost. In Exhibit 13, average variable cost is minimized at 2 units, whereas average total cost is the lowest at 3 units. Average total cost (ATC) is calculated by dividing total costs by quantity or by summing average fixed cost and average variable cost. For instance, in Exhibit 13, at 8 un

Flashcard 1464622255372

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Question
Base object : Besides it being an "object", it may also be

[...] If it is an item from your Peg System, then we call the base it peg.

In method of loci we call it locus (base has a position with respect to other objects).
a person, or something general like room, wall, ground, etc.

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Base object : Besides it being an "object", it may also be a person, or something general like room, wall, ground, etc. If it is an item from your Peg System, then we call t he base it peg. In method of loci we call it locus (base has a position with respect to other objects

r30's mnemonics

Flashcard 1477234527500

Tags
#matlab #programming
Question
The expression y>0returns a logical vector with 1s where sin(x) is positive, and 0s otherwise. Element-by-element multiplication by y with [operator] then picks out the positive elements of y
.*

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The expression y>0returns a logical vector with 1s where sin(x) is positive, and 0s otherwise. Element-by-element multiplication by y with .* then picks out the positive elements of y

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Flashcard 1479147916556

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#daniel-goleman #emotional-brain #emotional-iq #how-the-brain-grew #what-are-emotions-for #when-passions-overwhelm-reasons
Question
The most ancient root of our emotional life is in the sense [...]
of smell

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The most ancient root of our emotional life is in the sense of smell

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Flashcard 1480551435532

Tags
#bayes #programming #r #statistics
Question
A value represents the central tendency of the distribution if [close to where?]
the value is close to the highly probable values of the distribution

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A value represents the central tendency of the distribution if the value is close to the highly probable values of the distribution

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Annotation 1481823882508

#deeplearning #neuralnetworks
When A has more columns than rows, then solving a linear equation using the pseudoin v erse provides one of the man y p ossible solutions. Speciﬁcally , it pro vides the solution x = A + y with minimal Euclidean norm ||x||2 among all p ossible solutions

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Annotation 1481828601100

#deeplearning #neuralnetworks
the trace op erator provides an alternativ e w a y of writing the F rob enius norm of a matrix: ||A||F = Tr( AAT )

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Annotation 1481830173964

#deeplearning #neuralnetworks
the trace op erator is in v arian t to the transp ose op erator: T r(A) = T r(AT )

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Annotation 1481833319692

#deeplearning #neuralnetworks
This inv ariance to cyclic p erm utation holds even if the resulting pro duct has a diﬀeren t shap e. F or example, for A ∈ R m n × and B ∈ R n m × , w e ha v e T r(AB ) = T r( BA)

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Annotation 1481929002252

#deeplearning #neuralnetworks
T o b e a probability mass function on a random v ariable x , a function P m ust satisfy the follo wing prop erties:
• The domain of P must b e the set of all p ossible states of x.
• ∀x ∈ x , 0 ≤ P ( x ) ≤ 1 . An imp ossible ev en t has probabilit y and no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1
$$\sum_{x \in x}P(x) = 1$$ . W e refer to this prop erty as b eing normalized . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.

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Annotation 1481930575116

#deeplearning #neuralnetworks
W e can place a uniform distribution on x —that is, make each of its states equally lik ely—b y setting its probabilit y mass function to P (x = x i ) = 1/k

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Annotation 1481937128716

#deeplearning #neuralnetworks
The probability distribution o v er the subset is kno wn as the marginal probability distribution.

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Annotation 1481953643788

#deeplearning #neuralnetworks
The exp ectation or exp ected v alue of some function f ( x ) with resp ect to a probabilit y distribution P ( x ) is the a v erage or mean v alue that f tak es on when x is dra wn from . F or discrete v ariables this can be computed with a summation: Ex~P[f(x)]=$$\sum_{x} P(x)f(x)$$

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Flashcard 1483144826124

Tags
#deeplearning #neuralnetworks
Question
The transp ose of a matrix pro duct has a simple form [...] (2.9) This allo ws us to demonstrate equation , b y exploiting the fact that the v alue 2.8 of suc h a pro duct is a scalar and therefore equal to its o wn transp ose:$$x^Ty = (x^Ty)^T = y^T x$$
simple form: $$(AB)^{T} = B^{T}A^{T}$$ .

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The transp ose of a matrix pro duct has a simple form: $$(AB)^{T} = B^{T}A^{T}$$ . (2.9) This allo ws us to demonstrate equation , b y exploiting the fact that the v alue 2.8 of suc h a pro duct is a scalar and therefore equal to its o wn transp ose:$$x^Ty = (x^Ty)^T Original toplevel document (pdf) cannot see any pdfs Flashcard 1484186586380 Tags #biochem #biology #cell Question The β sheet is shown in (C) and (D). In this example, adjacent peptide chains run in [...] directions. Hydrogen-bonding between peptide bonds in different strands holds the individual polypeptide chains (strands) together in a β sheet, and the amino acid side chains in each strand alternately project above and below the plane of the sheet Answer opposite (antiparallel) status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it The β sheet is shown in (C) and (D). In this example, adjacent peptide chains run in opposite (antiparallel) directions. Hydrogen-bonding between peptide bonds in different strands holds the individual polypeptide chains (strands) together in a β sheet, and the amino acid side chains in each Original toplevel document (pdf) cannot see any pdfs Flashcard 1484269686028 Tags #deeplearning #neuralnetworks Question The [...] is commonly used in machine learning when the diﬀerence b etw een zero and nonzero elements is v ery imp ortan t. Answer L 1 norm status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it The L 1 norm is commonly used in machine learning when the diﬀerence b etw een zero and nonzero elements is v ery imp ortan t. Every time an element of x mo v es a w a y from 0 b y , the L 1 norm Original toplevel document (pdf) cannot see any pdfs Flashcard 1484322114828 Tags #deeplearning #neuralnetworks Question An [...] of a square matrix A is a non-zero vector v suc h that m ulti- plication b y A alters only the scale of v : Av = vλ Answer eigen v ector status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it An eigen v ector of a square matrix A is a non-zero vector v suc h that m ulti- plication b y alters only the scale of : A v Av v = λ Original toplevel document (pdf) cannot see any pdfs Flashcard 1484326046988 Tags #deeplearning #neuralnetworks Question Av = vλ . (2.39) The scalar λ is kno wn as the [...] corresp onding to this eigenv ector. (One can also ﬁnd a left eigen v ector suc h that v A = λ v , but we are usually concerned with righ t eigenv ectors Answer eigen v alue status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it Av v = λ . (2.39) The scalar λ is kno wn as the eigen v alue corresp onding to this eigenv ector. (One can also ﬁnd a left eigen v ector suc h that v A = λ v , but we are usually concerned with righ t eigenv ectors Original toplevel document (pdf) cannot see any pdfs Flashcard 1484540742924 Tags #biochem #biology #cell Question These principles are dramatically illustrated in the protein coat or capsid of many simple viruses, which takes the form of a [...] Answer hollow sphere based on an icosahedron status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it These principles are dramatically illustrated in the protein coat or capsid of many simple viruses, which takes the form of a hollow sphere based on an icosahedron Original toplevel document (pdf) cannot see any pdfs Flashcard 1484542315788 Tags #biochem #biology #cell Question The first large macromolecular aggregate shown to be capable of self-as- sembly from its component parts was [...] Answer tobacco mosaic virus (TMV ). status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it The first large macromolecular aggregate shown to be capable of self-as- sembly from its component parts was tobacco mosaic virus (TMV ). Original toplevel document (pdf) cannot see any pdfs Flashcard 1484543888652 Tags #biochem #biology #cell Question the simplest case, a long core protein or other macromolecule provides a scaffold that determines the extent of the final assembly. This is the mechanism that deter- mines the length of the [...] particle, where the RNA chain provides the core. Similarly, a core protein interacting with actin is thought to determine the length of the thin filaments in muscle. Answer TMV status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it the simplest case, a long core protein or other macromolecule provides a scaffold that determines the extent of the final assembly. This is the mechanism that deter- mines the length of the TMV particle, where the RNA chain provides the core. Similarly, a core protein interacting with actin is thought to determine the length of the thin filaments in muscle.</bo Original toplevel document (pdf) cannot see any pdfs Flashcard 1484545461516 Tags #biochem #biology #cell Question the simplest case, a long core protein or other macromolecule provides a scaffold that determines the extent of the final assembly. This is the mechanism that deter- mines the length of the TMV particle, where the RNA chain provides the core. Similarly, a core protein interacting with [...] is thought to determine the length of the thin filaments in muscle. Answer actin status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it er macromolecule provides a scaffold that determines the extent of the final assembly. This is the mechanism that deter- mines the length of the TMV particle, where the RNA chain provides the core. Similarly, a core protein interacting with <span>actin is thought to determine the length of the thin filaments in muscle.<span><body><html> Original toplevel document (pdf) cannot see any pdfs Flashcard 1484547034380 Tags #biochem #biology #cell Question Typically, hundreds of monomers will aggregate to form an unbranched fibrous structure that is several micrometers long and [...] in width Answer 5 to 15 nm status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it Typically, hundreds of monomers will aggregate to form an unbranched fibrous structure that is several micrometers long and 5 to 15 nm in width Original toplevel document (pdf) cannot see any pdfs Flashcard 1484548607244 Tags #biochem #biology #cell Question Typically, hundreds of monomers will aggregate to form an unbranched fibrous structure that is [...] long and 5 to 15 nm in width Answer several micrometers status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it Typically, hundreds of monomers will aggregate to form an unbranched fibrous structure that is several micrometers long and 5 to 15 nm in width Original toplevel document (pdf) cannot see any pdfs Flashcard 1484550180108 Tags #biochem #biology #cell Question A surprisingly large fraction of pro- teins have the potential to form [...] structures, because the short segment of the polypeptide chain that forms the spine of the fibril can have a variety of different sequences and follow one of several different paths (Figure 3–32). However, very few proteins will actually form this structure inside cells Answer amyloid fibril status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it A surprisingly large fraction of pro- teins have the potential to form such structures, because the short segment of the polypeptide chain that forms the spine of the fibril can have a variety of different sequences and follow one of several different paths ( Original toplevel document (pdf) cannot see any pdfs Flashcard 1484551752972 Tags #biochem #biology #cell Question A surprisingly large fraction of pro- teins have the potential to form Amyloid fibril structures, because [...] (Figure 3–32). However, very few proteins will actually form this structure inside cells Answer the short segment of the polypeptide chain that forms the spine of the fibril can have a variety of different sequences and follow one of several different paths status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it A surprisingly large fraction of pro- teins have the potential to form such structures, because the short segment of the polypeptide chain that forms the spine of the fibril can have a variety of different sequences and follow one of several different paths (Figure 3–32). However, very few proteins will actually form this structure inside cells Original toplevel document (pdf) cannot see any pdfs Flashcard 1484554112268 Tags #biochem #biology #cell Question A set of closely related diseases—[...] in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP Answer scra- pie status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular pr Original toplevel document (pdf) cannot see any pdfs Flashcard 1484555685132 Tags #biochem #biology #cell Question A set of closely related diseases—scra- pie in sheep, [...] disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP Answer Creutzfeldt–Jakob status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP</spa Original toplevel document (pdf) cannot see any pdfs Flashcard 1484557257996 Tags #biochem #biology #cell Question A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, [...] in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP Answer Kuru status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP Original toplevel document (pdf) cannot see any pdfs Flashcard 1484558830860 Tags #biochem #biology #cell Question A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and [...] (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP Answer bovine spongiform encephalopathy status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP Original toplevel document (pdf) cannot see any pdfs Flashcard 1484560403724 Tags #biochem #biology #cell Question A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a [...] Answer misfolded, aggregated form of a particular protein called PrP status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP Original toplevel document (pdf) cannot see any pdfs Flashcard 1484561976588 Tags #biochem #biology #cell Question Eukaryotic cells, for example, store many different peptide and protein hormones that they will secrete in specialized “secretory granules,” which package a high concentra- tion of their cargo in dense cores with a regular structure (see Figure 13–65). We now know that these structured cores consist of [...], which in this case have a structure that causes them to dissolve to release soluble cargo after being secreted by exocytosis to the cell exterior Answer amyloid fibrils status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it protein hormones that they will secrete in specialized “secretory granules,” which package a high concentra- tion of their cargo in dense cores with a regular structure (see Figure 13–65). We now know that these structured cores consist of <span>amyloid fibrils, which in this case have a structure that causes them to dissolve to release soluble cargo after being secreted by exocytosis to the cell exterior<span><body><html> Original toplevel document (pdf) cannot see any pdfs Flashcard 1484563549452 Tags #biochem #biology #cell Question Eukaryotic cells, for example, store many different peptide and protein hormones that they will secrete in specialized “secretory granules,” which package a high concentra- tion of their cargo in dense cores with a regular structure (see Figure 13–65). We now know that these structured cores consist of amyloid fibrils, which in this case have a structure that causes them [...] Answer to dissolve to release soluble cargo after being secreted by exocytosis to the cell exterior status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it nules,” which package a high concentra- tion of their cargo in dense cores with a regular structure (see Figure 13–65). We now know that these structured cores consist of amyloid fibrils, which in this case have a structure that causes them <span>to dissolve to release soluble cargo after being secreted by exocytosis to the cell exterior<span><body><html> Original toplevel document (pdf) cannot see any pdfs Flashcard 1484565122316 Tags #deeplearning #neuralnetworks Question When A has [...] then solving a linear equation using the pseudoin v erse provides one of the man y p ossible solutions. Speciﬁcally , it pro vides the solution x = A + y with minimal Euclidean norm || || x 2 among all p ossible solutions Answer more columns than rows, status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it When A has more columns than rows, then solving a linear equation using the pseudoin v erse provides one of the man y p ossible solutions. Speciﬁcally , it pro vides the solution x = A + y with minimal Euclidean norm || Original toplevel document (pdf) cannot see any pdfs Flashcard 1484566695180 Tags #deeplearning #neuralnetworks Question When A has more columns than rows, then solving a linear equation using the [...] provides one of the man y p ossible solutions. Speciﬁcally , it pro vides the solution x = A + y with minimal Euclidean norm || || x 2 among all p ossible solutions Answer pseudoin v erse status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it When A has more columns than rows, then solving a linear equation using the pseudoin v erse provides one of the man y p ossible solutions. Speciﬁcally , it pro vides the solution x = A + y with minimal Euclidean norm || || x 2 among all p ossible solutions Original toplevel document (pdf) cannot see any pdfs Flashcard 1484570627340 Tags #deeplearning #neuralnetworks Question When A has more columns than rows, then solving a linear equation using the pseudoin v erse provides one of the man y p ossible solutions. Speciﬁcally , it pro vides [...] Answer the solution x = A + y with minimal Euclidean norm ||x||2 among all p ossible solutions status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it When A has more columns than rows, then solving a linear equation using the pseudoin v erse provides one of the man y p ossible solutions. Speciﬁcally , it pro vides the solution x = A + y with minimal Euclidean norm ||x|| 2 among all p ossible solutions Original toplevel document (pdf) cannot see any pdfs Flashcard 1484572724492 Tags #deeplearning #neuralnetworks Question the [...] provides an alternativ e w a y of writing the F rob enius norm of a matrix: || || A F = T r( AA ) Answer trace op erator status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it the trace op erator provides an alternativ e w a y of writing the F rob enius norm of a matrix: || || A F = T r( AA ) Original toplevel document (pdf) cannot see any pdfs Flashcard 1484574297356 Tags #deeplearning #neuralnetworks Question the trace op erator provides an alternativ e w a y of writing the [...] of a matrix: || || A F = T r( AA ) Answer F rob enius norm status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it the trace op erator provides an alternativ e w a y of writing the F rob enius norm of a matrix: || || A F = T r( AA ) Original toplevel document (pdf) cannot see any pdfs Flashcard 1484576656652 Tags #deeplearning #neuralnetworks Question the trace op erator provides an alternativ e w a y of writing the F rob enius norm of a matrix: ||A||F = [...] Answer Tr( AAT ) status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it the trace op erator provides an alternativ e w a y of writing the F rob enius norm of a matrix: ||A|| F = Tr( AA T ) Original toplevel document (pdf) cannot see any pdfs Flashcard 1484578229516 Tags #deeplearning #neuralnetworks Question the trace op erator is in v arian t to the [...]: T r( ) = T r( A A ) Answer transp ose op erator status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it the trace op erator is in v arian t to the transp ose op erator: T r( ) = T r( A A ) Original toplevel document (pdf) cannot see any pdfs Flashcard 1484579802380 Tags #deeplearning #neuralnetworks Question the [...] is in v arian t to the transp ose op erator: T r( ) = T r( A A ) Answer trace op erator status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it the trace op erator is in v arian t to the transp ose op erator: T r( ) = T r( A A ) Original toplevel document (pdf) cannot see any pdfs Flashcard 1484582161676 Tags #deeplearning #neuralnetworks Question the trace op erator is in v arian t to the transp ose op erator: T r(A) = [...] Answer T r(AT ) status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it the trace op erator is in v arian t to the transp ose op erator: T r(A) = T r(A T ) Original toplevel document (pdf) cannot see any pdfs Flashcard 1484584520972 Tags #deeplearning #neuralnetworks Question This inv ariance to cyclic p erm utation holds even if [...] F or example, for A ∈ R m n × and B ∈ R n m × , w e ha v e T r(AB ) = T r( BA) Answer the resulting pro duct has a diﬀeren t shap e. status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it This inv ariance to cyclic p erm utation holds even if the resulting pro duct has a diﬀeren t shap e. F or example, for A ∈ R m n × and B ∈ R n m × , w e ha v e T r(AB ) = T r( BA) Original toplevel document (pdf) cannot see any pdfs Flashcard 1484586093836 Tags #deeplearning #neuralnetworks Question One simple mac hine learning algorithm, [...] can b e deriv ed using only knowledge of basic linear algebra Answer principal components analysis or PCA status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it One simple mac hine learning algorithm, principal components analysis or PCA can b e deriv ed using only knowledge of basic linear algebra Original toplevel document (pdf) cannot see any pdfs Flashcard 1484587666700 Tags #deeplearning #neuralnetworks Question Lossy compression means [...] Answer storing the p oints in a wa y that requires less memory but ma y lose some precision status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it Lossy compression means storing the p oints in a wa y that requires less memory but ma y lose some precision Original toplevel document (pdf) cannot see any pdfs Flashcard 1484589239564 Tags #deeplearning #neuralnetworks Question [...] means storing the p oints in a wa y that requires less memory but ma y lose some precision Answer Lossy compression status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it Lossy compression means storing the p oints in a wa y that requires less memory but ma y lose some precision Original toplevel document (pdf) cannot see any pdfs Flashcard 1484590812428 Tags #deeplearning #neuralnetworks Question T o k eep the enco ding problem easy , PCA [...] Answer constrains the colum ns of D to b e orthogonal to eac h other. status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it T o k eep the enco ding problem easy , PCA constrains the colum ns of D to b e orthogonal to eac h other. Original toplevel document (pdf) cannot see any pdfs Flashcard 1484592385292 Tags #deeplearning #neuralnetworks Question There are three p ossible sources of uncertain t y: [...] 2. Incomplete observ ability . Ev en deterministic systems can app ear sto chastic when w e cannot observ e all of the v ariables that drive the b ehavior of the system. F or example, in the Mont y Hall problem, a game sho w con testan t is ask ed to choose b etw een three do ors and wins a prize held b ehind the c hosen do or. T w o do ors lead to a goat while a third leads to a car. The outcome giv en the contestan t’s c hoice is deterministic, but from the con testan t’s p oin t of view, the outcome is uncertain. 3. Incomplete mo deling. When we use a mo del that must discard some of the information we hav e observ ed, the discarded information results in uncertain t y in the mo del’s predictions. Answer 1. Inheren t stochasticit y in the system b eing mo deled. F or example, most in terpretations of quantum mechanics describ e the dynamics of subatomic particles as b eing probabilistic. W e can also create theoretical scenarios that w e p ostulate to ha v e random dynamics, such as a hypothetical card game where w e assume that the cards are truly sh uﬄed in to a random order. status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it There are three p ossible sources of uncertain t y: 1. Inheren t stochasticit y in the system b eing mo deled. F or example, most in terpretations of quantum mechanics describ e the dynamics of subatomic particles as b eing probabilistic. W e can also create theoretical scenarios that w e p ostulate to ha v e random dynamics, such as a hypothetical card game where w e assume that the cards are truly sh uﬄed in to a random order. 2. Incomplete observ ability . Ev en deterministic systems can app ear sto chastic when w e cannot observ e all of the v ariables that drive the b ehavior of the system. F or example, i Original toplevel document (pdf) cannot see any pdfs Flashcard 1484593958156 Tags #deeplearning #neuralnetworks Question There are three p ossible sources of uncertain t y: 1. Inheren t stochasticit y in the system b eing mo deled. F or example, most in terpretations of quantum mechanics describ e the dynamics of subatomic particles as b eing probabilistic. W e can also create theoretical scenarios that w e p ostulate to ha v e random dynamics, such as a hypothetical card game where w e assume that the cards are truly sh uﬄed in to a random order. [...] 3. Incomplete mo deling. When we use a mo del that must discard some of the information we hav e observ ed, the discarded information results in uncertain t y in the mo del’s predictions. Answer 2. Incomplete observ ability . Ev en deterministic systems can app ear sto chastic when w e cannot observ e all of the v ariables that drive the b ehavior of the system. F or example, in the Mont y Hall problem, a game sho w con testan t is ask ed to choose b etw een three do ors and wins a prize held b ehind the c hosen do or. T w o do ors lead to a goat while a third leads to a car. The outcome giv en the contestan t’s c hoice is deterministic, but from the con testan t’s p oin t of view, the outcome is uncertain. status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it of subatomic particles as b eing probabilistic. W e can also create theoretical scenarios that w e p ostulate to ha v e random dynamics, such as a hypothetical card game where w e assume that the cards are truly sh uﬄed in to a random order. <span>2. Incomplete observ ability . Ev en deterministic systems can app ear sto chastic when w e cannot observ e all of the v ariables that drive the b ehavior of the system. F or example, in the Mont y Hall problem, a game sho w con testan t is ask ed to choose b etw een three do ors and wins a prize held b ehind the c hosen do or. T w o do ors lead to a goat while a third leads to a car. The outcome giv en the contestan t’s c hoice is deterministic, but from the con testan t’s p oin t of view, the outcome is uncertain. 3. Incomplete mo deling. When we use a mo del that must discard some of the information we hav e observ ed, the discarded information results in uncertain t y in the mo del’s prediction Original toplevel document (pdf) cannot see any pdfs Flashcard 1484595531020 Tags #deeplearning #neuralnetworks Question There are three p ossible sources of uncertain t y: 1. Inheren t stochasticit y in the system b eing mo deled. F or example, most in terpretations of quantum mechanics describ e the dynamics of subatomic particles as b eing probabilistic. W e can also create theoretical scenarios that w e p ostulate to ha v e random dynamics, such as a hypothetical card game where w e assume that the cards are truly sh uﬄed in to a random order. 2. Incomplete observ ability . Ev en deterministic systems can app ear sto chastic when w e cannot observ e all of the v ariables that drive the b ehavior of the system. F or example, in the Mont y Hall problem, a game sho w con testan t is ask ed to choose b etw een three do ors and wins a prize held b ehind the c hosen do or. T w o do ors lead to a goat while a third leads to a car. The outcome giv en the contestan t’s c hoice is deterministic, but from the con testan t’s p oin t of view, the outcome is uncertain. [...] Answer 3. Incomplete mo deling. When we use a mo del that must discard some of the information we hav e observ ed, the discarded information results in uncertain t y in the mo del’s predictions. status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it s and wins a prize held b ehind the c hosen do or. T w o do ors lead to a goat while a third leads to a car. The outcome giv en the contestan t’s c hoice is deterministic, but from the con testan t’s p oin t of view, the outcome is uncertain. <span>3. Incomplete mo deling. When we use a mo del that must discard some of the information we hav e observ ed, the discarded information results in uncertain t y in the mo del’s predictions.<span><body><html> Original toplevel document (pdf) cannot see any pdfs Flashcard 1484597103884 Tags #deeplearning #neuralnetworks Question A random v ariable is [...] Answer a v ariable that can take on diﬀerent v alues randomly status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it A random v ariable is a v ariable that can take on diﬀerent v alues randomly Original toplevel document (pdf) cannot see any pdfs Flashcard 1484598676748 Tags #deeplearning #neuralnetworks Question On its o wn, a random v ariable is just [...] it m ust b e coupled with a probability distribution that sp eciﬁes how likely each of these states are. Answer a description of the states that are p ossible; status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it On its o wn, a random v ariable is just a description of the states that are p ossible; it m ust b e coupled with a probability distribution that sp eciﬁes how likely each of these states are. Original toplevel document (pdf) cannot see any pdfs Flashcard 1484600249612 Tags #deeplearning #neuralnetworks Question On its o wn, a random v ariable is just a description of the states that are p ossible; it m ust b e [...] Answer coupled with a probability distribution that sp eciﬁes how likely each of these states are. status measured difficulty not learned 37% [default] 0 Parent (intermediate) annotation Open it On its o wn, a random v ariable is just a description of the states that are p ossible; it m ust b e coupled with a probability distribution that sp eciﬁes how likely each of these states are. Original toplevel document (pdf) cannot see any pdfs Flashcard 1484603395340 Tags #deeplearning #neuralnetworks Question T o b e a probability mass function on a random v ariable x , a function P m ust satisfy the follo wing prop erties: • The domain of P [...] • ∀x ∈ x , 0 ≤ P ( x ) ≤ 1 . An imp ossible ev en t has probabilit y and no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1 \(\sum_{x \in x}P(x) = 1$$ . W e refer to this prop erty as b eing normalized . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.
must b e the set of all p ossible states of x.

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T o b e a probability mass function on a random v ariable x , a function P m ust satisfy the follo wing prop erties: • The domain of P must b e the set of all p ossible states of x. • ∀x ∈ x , 0 ≤ P ( x ) ≤ 1 . An imp ossible ev en t has probabilit y and no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has proba

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Flashcard 1484604968204

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#deeplearning #neuralnetworks
Question
T o b e a probability mass function on a random v ariable x , a function P m ust satisfy the follo wing prop erties:
• The domain of P must b e the set of all p ossible states of x.
• ∀x ∈ x , [...]
$$\sum_{x \in x}P(x) = 1$$ . W e refer to this prop erty as b eing normalized . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.
0 ≤ P ( x ) ≤ 1 . An imp ossible ev en t has probabilit y and no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1

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T o b e a probability mass function on a random v ariable x , a function P m ust satisfy the follo wing prop erties: • The domain of P must b e the set of all p ossible states of x. • ∀x ∈ x , 0 ≤ P ( x ) ≤ 1 . An imp ossible ev en t has probabilit y and no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1 • ∑x∈xP(x)=1∑x∈xP(x)=1 . W e refer to this prop erty as b eing normalized . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y o

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Flashcard 1484608638220

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#deeplearning #neuralnetworks
Question
T o b e a probability mass function on a random v ariable x , a function P m ust satisfy the follo wing prop erties:
• The domain of P must b e the set of all p ossible states of x.
• ∀x ∈ x , 0 ≤ P ( x ) ≤ 1 . An imp ossible ev en t has probabilit y and no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1
$$\sum_{x \in x}P(x) = 1$$ . W e refer to this prop erty as b eing [...] . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.
normalized

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no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1 • ∑x∈xP(x)=1∑x∈xP(x)=1 . W e refer to this prop erty as b eing <span>normalized . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.<span><body><html>

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Flashcard 1484610211084

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#deeplearning #neuralnetworks
Question
T o b e a probability mass function on a random v ariable x , a function P m ust satisfy the follo wing prop erties:
• The domain of P must b e the set of all p ossible states of x.
• ∀x ∈ x , 0 ≤ P ( x ) ≤ 1 . An imp ossible ev en t has probabilit y and no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1
$$\sum_{x \in x}P(x) = 1$$ . W e refer to this prop erty as b eing normalized . Without this prop ert y , we could [...]
obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.

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ewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1 • ∑x∈xP(x)=1∑x∈xP(x)=1 . W e refer to this prop erty as b eing normalized . Without this prop ert y , we could <span>obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.<span><body><html>

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Flashcard 1484612570380

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Question
W e can place a uniform distribution on x —that is, make each of its states equally lik ely—b y setting its probabilit y mass function to [...]
P (x = x i ) = 1/k

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W e can place a uniform distribution on x —that is, make each of its states equally lik ely—b y setting its probabilit y mass function to P (x = x i ) = 1/k

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Flashcard 1484614929676

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The probability distribution o v er the subset is kno wn as the [...]
marginal probability distribution.

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The probability distribution o v er the subset is kno wn as the marginal probability distribution.

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Flashcard 1484616502540

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#deeplearning #neuralnetworks
Question
The name “[...]” comes from the pro cess of computing marginal probabilities on pap er. When the v alues of P ( x y , ) are written in a grid with diﬀeren t v alues of x in rows and diﬀerent v alues of y in columns, it is natural to sum across a row of the grid, then write P ( x ) in the margin of the pap er just to the righ t of the ro w
marginal probabilit y

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The name “marginal probabilit y” comes from the pro cess of computing marginal probabilities on pap er. When the v alues of P ( x y , ) are written in a grid with diﬀeren t v alues of x in rows and diﬀerent v alues of

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Flashcard 1484618075404

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The name “marginal probabilit y” comes from [...]. When the v alues of P ( x y , ) are written in a grid with diﬀeren t v alues of x in rows and diﬀerent v alues of y in columns, it is natural to sum across a row of the grid, then write P ( x ) in the margin of the pap er just to the righ t of the ro w
the pro cess of computing marginal probabilities on pap er

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The name “marginal probabilit y” comes from the pro cess of computing marginal probabilities on pap er. When the v alues of P ( x y , ) are written in a grid with diﬀeren t v alues of x in rows and diﬀerent v alues of y in columns, it is natural to sum across a row of the grid, then writ

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Flashcard 1484619648268

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Question
The name “marginal probabilit y” comes from the pro cess of computing marginal probabilities on pap er. When the v alues of P ( x y , ) are written in a grid with diﬀeren t v alues of x in rows and diﬀerent v alues of y in columns, it is natural to [...], then write P ( x ) in the margin of the pap er just to the righ t of the ro w
sum across a row of the grid

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me “marginal probabilit y” comes from the pro cess of computing marginal probabilities on pap er. When the v alues of P ( x y , ) are written in a grid with diﬀeren t v alues of x in rows and diﬀerent v alues of y in columns, it is natural to <span>sum across a row of the grid, then write P ( x ) in the margin of the pap er just to the righ t of the ro w<span><body><html>

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Flashcard 1484621221132

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The exp ectation or exp ected v alue of some function f ( x ) with resp ect to a probabilit y distribution P ( x ) is the [...] that f tak es on when x is dra wn from . F or discrete v ariables this can be computed with a summation: P E x ∼ P [ ( )] = f x x P x f x , ( ) ( )
a v erage or mean v alue

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The exp ectation or exp ected v alue of some function f ( x ) with resp ect to a probabilit y distribution P ( x ) is the a v erage or mean v alue that f tak es on when x is dra wn from . F or discrete v ariables this can be computed with a summation: P E x ∼ P [ ( )] = f x x P x f x , ( ) ( )

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Flashcard 1484623580428

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Question
The exp ectation or exp ected v alue of some function f ( x ) with resp ect to a probabilit y distribution P ( x ) is the a v erage or mean v alue that f tak es on when x is dra wn from . F or discrete v ariables this can be computed with [...]
a summation: Ex~P[f(x)]=$$\sum_{x} P(x)f(x)$$xP(x)f(x)

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pan>The exp ectation or exp ected v alue of some function f ( x ) with resp ect to a probabilit y distribution P ( x ) is the a v erage or mean v alue that f tak es on when x is dra wn from . F or discrete v ariables this can be computed with <span>a summation: E x~P[f(x)] =∑xP(x)f(x)∑xP(x)f(x)<span><body><html>

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Flashcard 1484865015052

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#has-images
c

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Flashcard 1485461654796

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#has-images

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Flashcard 1485822364940

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#cement #has-images #ies
C

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Flashcard 1485828656396

Question
Definizione di Sistema Distribuito.
Un sistema distribuito è:
Una collezione di computer indipendenti che appaiono agli utenti come un sistema singolo coerente .

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Annotation 1485833899276

Trasparenza rispetto all'Accesso: Nasconde le differenze nella rappresentazione dei dati e su come una risorsa è acceduta.

Se usiamo dropbox noi non sappiamo le caratteristiche dell'infrastruttura, sappiamo solo cosa fare per accedere (login)

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Annotation 1485836258572

Trasparenza rispetto alla Locazione: Nasconde dove una risorsa si trovi.
Se accediamo diverse volte a Gmail, probabilmente accediamo a server fisici diversi... Ma questo è trasparente all'utente.

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Annotation 1485838617868

Trasparenza alla Migrazione: Nasconde che una risorsa si possa spostare in una differente locazione.
Sposta le risorse mentre noi le accediamo... Senza che ce ne possiamo accorgerne.

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Annotation 1485840977164

Trasparenza alla Rilocazione: Nasconde che una risorsa si possa spostare in una differente locazione mentre viene usata.

Ci sono cloud che spostano la macchina virtuale su un'altra macchina fisica nel frattempo che io la sto usando.

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Annotation 1485843336460

Concorrenza Nasconde che una risorsa possa essere condivisa da più utenti concorrenti

Non fa a vedere all'utente che la risorsa la stanno accedendo anche altri.. Facile in lettura ma non se qualcuno accede in scrittura.

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Flashcard 1485845695756

Question
Trasparenza alla Persistenza
Nasconde se una risorsa (software) è in memoria o nel disco.

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Flashcard 1485848317196

Question
Definizione di Lamport di sistema distribuito.
Tu sai di averne (usarne) uno quando il guasto di un computer di cui non hai mai sentito parlare non ti permette di fare il tuo lavoro.

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Annotation 1485850152204

Servizi Centralizzati Un solo server per tutti gli utenti. Certamente non è scalabile... All'aumentare degli utenti il sistema non scala e si siede.

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Annotation 1485852511500

Dati Centralizzati Un singolo elenco telefonico on line

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Annotation 1485854084364

Algoritmi Centralizzati Effettuare il routing basato su informazioni complete

Algoritmo che svolge la maggior parte sua computazione in una macchina (in un metodo, in un singolo pezzo di codice) .

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Annotation 1485856443660

Algoritmi Decentralizzati
Un algoritmo non viene eseguito da una sola macchina ma è diviso in tanti pezzi che sono eseguiti da computer diversi.

1.  Nessuna macchina ha l informazione completa sullo stato del sistema
Esempio: Non esiste un computer che conosce tutto internet... Il concetto è che siccome in un sistema distribuito non si può conoscere da un punto tutto lo stato della rete, allora gli algoritmi fanno l'assunzione che non si può avere da un nodo l'informazione completa sullo stato del sistema..

2.  Le macchine prendono decisioni sulla base di informazioni locali.

3.  Il fallimento di una macchina non fa fallire l algoritmo.
Esempio: Abbiamo preso una matrice e l'abbiamo divisa in righe. E abbiamo dato ad ogni nodo una riga. Se un nodo fallisce abbiamo perso la somma di una riga.
Se invece, abbiamo un nodo che fa la somma di tutte le righe, se fallisce questo fallisce tutto l'algoritmo.

4.  Non vi è una assunzione di un clock globale.
Le scelte dell'algoritmo non devono dipendere dal clock del computer... Perché da macchina a macchina cambia.. Quindi porterebbe ad inconsistenza.
Non posso assumere che vi sia un clock globale.

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Annotation 1485858802956

Più grande è il sistema, maggiore è l'incertezza. "Caspita, allora siamo nei guai !!! "

Nonostanze c'è l'incertezza, i sistemi distribuiti funzionano bene gestendo l'incertezza.

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Annotation 1485861948684

La differenza tra chi tra: a)  un server o b)  un client controlla le forme che sono state riempite.

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Flashcard 1485868502284

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#cement #has-images
B

status measured difficulty not learned 37% [default] 0

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Flashcard 1485871910156

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C

status measured difficulty not learned 37% [default] 0

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Flashcard 1485875318028

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A-3,B-1,C-4,D-2

status measured difficulty not learned 37% [default] 0

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Flashcard 1485878725900

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#cement #has-images
B

status measured difficulty not learned 37% [default] 0

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Flashcard 1485882133772

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C

status measured difficulty not learned 37% [default] 0

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Flashcard 1485885541644

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#cement #has-images
D

50 kg------------>0.035m3
1429 kg------------>1m3
1 tonne=1000kg

1000kg------------>0.699m3
No. of bags=0.699/0.035 =20 bags

status measured difficulty not learned 37% [default] 0

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Flashcard 1485888949516

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C

the normal consistency for OPC ranges from 26 to 33%.
1. Take 400 g of cement and place it in the enameled tray.
Mix about 25% water by weight of dry cement thoroughly to get a cement paste. Total time taken to obtain thoroughly mixed water cement paste i.e. “Gauging time” should not be more than 3 to 5 minutes.
• ​​ the depth of penetration becomes 33 to 35 mm.

Where,

C=Quantity of cement used

status measured difficulty not learned 37% [default] 0

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Annotation 1485904153868

In computer architecture, a bus[1] (a contraction of the Latin omnibus) is a communication system that transfers data between components inside a computer, or between computers. This expression covers all related hardware components (wire, optical fiber, etc.) and software, including communication protocols.[2]

Bus (computing) - Wikipedia
ed. (February 2013) (Learn how and when to remove this template message) [imagelink] 4 PCI Express bus card slots (from top to 2nd bottom: x4, x16, x1 and x16), compared to a 32-bit conventional PCI bus card slot (very bottom) <span>In computer architecture, a bus [1] (a contraction of the Latin omnibus) is a communication system that transfers data between components inside a computer, or between computers. This expression covers all related hardware components (wire, optical fiber, etc.) and software, including communication protocols. [2] Early computer buses were parallel electrical wires with multiple connections, but the term is now used for any physical arrangement that provides the same logical function as a paral

Annotation 1485909396748

In computer architecture, a bus is a communication system that transfers data between components inside a computer, or between computers.

Parent (intermediate) annotation

Open it
In computer architecture, a bus [1] (a contraction of the Latin omnibus) is a communication system that transfers data between components inside a computer, or between computers. This expression covers all related hardware components (wire, optical fiber, etc.) and software, including communication protocols. [2]

Original toplevel document

Bus (computing) - Wikipedia
ed. (February 2013) (Learn how and when to remove this template message) [imagelink] 4 PCI Express bus card slots (from top to 2nd bottom: x4, x16, x1 and x16), compared to a 32-bit conventional PCI bus card slot (very bottom) <span>In computer architecture, a bus [1] (a contraction of the Latin omnibus) is a communication system that transfers data between components inside a computer, or between computers. This expression covers all related hardware components (wire, optical fiber, etc.) and software, including communication protocols. [2] Early computer buses were parallel electrical wires with multiple connections, but the term is now used for any physical arrangement that provides the same logical function as a paral

Flashcard 1485926960396

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status measured difficulty not learned 37% [default] 0