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Flashcard 1429070548236

Tags
#sister-miriam-joseph #trivium
Question
Valuable goods are those which are not only desired for their own sake but which [...] For instance, [...] are valuable goods.
Answer
increase the intrinsic worth of their possessor.

knowledge, virtue, and health.


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Valuable goods are those which are not only desired for their own sake but which increase the intrinsic worth of their possessor. For instance, knowledge, virtue, and health are valuable goods.

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Flashcard 1442931412236

Tags
#estructura-interna-de-las-palabras #formantes-morfológicos #gramatica-española #la #morfología #tulio
Question

Para establecer la estructura interna de las palabras, la morfología se ocupa de:

a. identificar [...]

b. determinar las posibles variaciones que éstos presenten;

c. describir los procesos involucrados;

d. reconocer la organización de las palabras.

Answer
los formantes morfológicos;


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Para establecer la estructura interna de las palabras, la morfología se ocupa de: a . identificar los formantes morfológicos; b . determinar las posibles variaciones que éstos presenten; c . describir los procesos involucrados; d . reconocer la organización de las palabras

Original toplevel document

La estructura interna de la palabra
1. Los formantes morfológicos Una palabra tiene estructura interna cuando contiene más de un formante morfológico. Un formante morfológico o morfema es una unidad mínima que consta de una forma fonética y de un significado. Comparemos las siguientes palabras: gota, gotas, gotita, gotera, cuentagotas. Gota es la única de estas palabras que consta de un solo formante. Carece, entonces, de estructura interna. Es una palabra simple. Todas las otras palabras tienen estructura interna. [31] Los formantes que pueden aparecer como palabras independientes son formas libres. Los otros, los que necesariamente van adosados a otros morfe- mas, son formas ligadas. Cuentagotas contiene dos formantes que pueden aparecer cada uno como palabra independiente. Es una palabra compuesta. Gotas, gotita y gotera también contienen dos formantes, pero uno de ellos (-s, -ita, -era) nunca puede ser una palabra independiente. Son formas ligadas que se denominan afijos. Algunos afijos van pospuestos a la base (gota), como los de nuestros ejemplos: son los s u f i j o s . Otros afijos la preceden: in-útil, des-contento, a-político: Son los prefijos. Las palabras que contienen un afijo se denominan palabras complejas. Del inventario de formantes reconocidos, reconoceremos dos clases: a. Algunos son formantes léxicos: tienen un significado léxico, que se define en el diccionario: gota, cuenta. Se agrupan en clases abiertas. Pertenecen a una clase particular de palabras: sustantivos (gota), adjetivos (útil), adverbios (ayer), verbos (cuenta). Pueden ser: - palabras simples (gota, útil, ayer); - base a la que se adosan los afijos en palabras complejas (got-, politic-); - parte de una palabra, compuesta (cuenta, gotas). b. Otros son formantes gramaticales: tienen significado gramatical, no léxico. Se agrupan en clases cerradas. Pueden ser: - palabras independientes: preposiciones (a, de, por), conjunciones (que, si); - afijos en palabras derivadas (-s, -ero, in-, des-); - menos frecuentemente, formantes de compuestos (aun-que, por-que, si-no). Entre las palabras no simples consideradas hasta aquí, cada una contenía sólo dos formantes. En otras un mismo tipo de formantes se repite: - sufijos: region-al-izar, util-iza-ble; - prefijos: des-com-poner. ex-pro-soviético, o también formantes de diferentes tipos pueden combinarse entre sí: - prefijo y sufijo: des-leal-tad, em-pobr-ecer; - palabra compuesta y sufijo: rionegr-ino, narcotrafic-ante. En la combinación de prefijación y sufijación, se distinguen dos casos, ilustrados en nuestros ejemplos. En deslealtad, la aplicación de cada uno de los afijos da como resultado una palabra bien formada: si aplicamos sólo el prefijo se obtiene el adjetivo desleal; si aplicamos sólo el sufijo el resultado será el sustantivo lealtad. En cambio, en empobrecer, si se aplica sólo un afijo [32] el resultado no será una palabra existente: *empobre, *pobrecer. Prefijo y sufijo se aplican simultáneamente, constituyendo un único formante morfológico – discontinuo– que se añade a ambos lados de la base léxica. Este segundo caso se denomina parasíntesis. Para establecer la estructura interna de las palabras, la morfología se ocupa de: a. identificar los formantes morfológicos; b. determinar las posibles variaciones que éstos presenten; c. describir los procesos involucrados; d. reconocer la organización de las palabras. 2. Identificación de los formantes morfológicos Comparemos ahora las siguientes palabras: sol, sol-ar; sol-azo, quita- sol, gira-sol, solter-o, solaz. En las







#deeplearning #neuralnetworks
The co v ariance matrix of a random vector x ∈ R n is an n n × matrix, suc h that Cov(x)i,j = Cov( x i , x j ) . (3.14) The diagonal elements of the covariance give the variance: Cov( x i , x i ) = Var( x i )

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The Bernoulli distribution is a distribution ov er a single binary random v ariable. It is controlled by a single parameter φ ∈ [0 , 1] , whic h gives the probability of the random v ariable b eing equal to 1. It has the following prop erties:
P (x = 1) = φ
P (x = 0) = 1-φ
P (x = x ) = φ x (1 − φ) 1 − x
E x [x] = φ
V ar (x) = φ (1− φ)

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#deeplearning #neuralnetworks
In some cases, we wish to sp ecify that all of the mass in a probabilit y distribution clusters around a single p oin t. This can b e accomplished b y defining a PDF using the Dirac delta function, : p(x) = δ(x-µ) The Dirac delta function is defined such that it is zero-v alued everywhere except 0, y et integrates to 1.

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Certain functions arise often while working with probability distributions, especially the probabilit y distributions used in deep learning models. One of these functions is the : logistic sigmoid σ(x) = 1/(1 + exp(− x))

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#deeplearning #neuralnetworks
Another commonly encountered function is the softplus function ( , Dugas et al. 2001 ): ζ(x) = log (1 + exp(x)) The softplus function can be useful for pro ducing the β or σ parameter of a normal distribution b ecause its range is (0 , ∞ )

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#deeplearning #neuralnetworks
Just as x can b e recov ered from its p ositive part and negativ e part via the iden tit y x + − x = x , it is also p ossible to reco v er x using the same relationship b et w een and ζ (x), ζ ( −x)

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#deeplearning #neuralnetworks
Supp ose we ha v e t w o random v ariables, x and y , suc h that y = g ( x ) , where g is an inv ertible, con- tin uous, differen tiable transformation. One migh t exp ect that p y ( y ) = p x ( g − 1 ( y )) . This is actually not the case.

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#deeplearning #neuralnetworks
In order to satisfy all three of these prop erties, w e define the self-information of an event x = x to be I x = −log P(x)

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Flashcard 1484231150860

Tags
#biochem #biology #cell
Question
most proteins are composed of a series of protein domains, in which different regions of the polypeptide chain fold independently to form compact structures. Such multidomain proteins are believed to have originated from the [process] , cre- ating a new gene.
Answer
accidental joining of the DNA sequences that encode each domain


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dy>most proteins are composed of a series of protein domains, in which different regions of the polypeptide chain fold independently to form compact structures. Such multidomain proteins are believed to have originated from the accidental joining of the DNA sequences that encode each domain, cre- ating a new gene.<body><html>

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Flashcard 1484315036940

Tags
#deeplearning #neuralnetworks
Question
A vector x and a vector y are [...] to each other if xTy = 0 .
Answer
orthogonal


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A vector x and a vector y are orthogonal to each other if x y = 0 .

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Flashcard 1484402593036

Tags
#bayes #programming #r #statistics
Question
In other words, the normalizer for the beta distribution is the [equation]
Answer
beta function \(B(a,b) = \int d\theta \space \theta^{a-1}(1-\theta)^{b-1}\)


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In other words, the normalizer for the beta distribution is the beta function \(B(a,b) = \int d\theta \space \theta^{a-1}(1-\theta)^{b-1}\)

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Flashcard 1484429856012

Tags
#bayes #programming #r #statistics
Question
The standard deviation of the beta distribution is [...] . Notice that the standard deviation gets smaller when the concentration κ = a + b gets larger.
Answer
\(\sqrt{μ(1 − μ)/(a + b +1)}\)


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The standard deviation of the beta distribution is μ(1−μ)/(a+b+1)−−−−−−−−−−−−−−−−√μ(1−μ)/(a+b+1). Notice that the standard deviation gets smaller when the concentration κ = a + b gets larger.

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Bacopa Monnieri is an extract from the Brahmi plant. According to WebMD, Bacopa is used for a wide variety of purposes, including as a supplemental Alzheimer’s treatment and way to reduce anxiety.

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How to Make Your Own Smart Drugs
tion. Research has also determined that this supplement can provide significant memory improvements in Alzheimer’s and vascular dementia patients. There are also generous amounts in the adaptogenic herb complex TianChi. 2. Bacopa Monnieri <span>Bacopa Monnieri is an extract from the Brahmi plant. According to WebMD, Bacopa is used for a wide variety of purposes, including as a supplemental Alzheimer’s treatment and way to reduce anxiety. Evidence suggests that this natural nootropic is effective at improving memory and hand-eye coordination. There have also been some studies that link Bacopa with a reduction in anxiety,




Flashcard 1486207978764

Question
Bacopa Monnieri is an extract from the Brahmi plant. According to WebMD, Bacopa is used for a wide variety of purposes, including as a supplemental Alzheimer’s treatment and way to reduce anxiety.
Answer
[default - edit me]


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Bacopa Monnieri is an extract from the Brahmi plant. According to WebMD, Bacopa is used for a wide variety of purposes, including as a supplemental Alzheimer’s treatment and way to reduce anxiety.

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How to Make Your Own Smart Drugs
tion. Research has also determined that this supplement can provide significant memory improvements in Alzheimer’s and vascular dementia patients. There are also generous amounts in the adaptogenic herb complex TianChi. 2. Bacopa Monnieri <span>Bacopa Monnieri is an extract from the Brahmi plant. According to WebMD, Bacopa is used for a wide variety of purposes, including as a supplemental Alzheimer’s treatment and way to reduce anxiety. Evidence suggests that this natural nootropic is effective at improving memory and hand-eye coordination. There have also been some studies that link Bacopa with a reduction in anxiety,







Flashcard 1486210600204

Question
For example, Piracetam was one of the first lab created compounds specifically designed to enhance cognitive performance, and although it is a synthesized chemical (with chemical name 2-oxo-1-pyrrolidine acetamide) it is generally regarded as being safe.
Answer
[default - edit me]


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How to Make Your Own Smart Drugs
ts together to achieve some pretty cool results. Happy blendin'. ————————- Synthetic vs. Natural Nootropics There are numerous synthetic smart drugs that are utilized nowadays by people from all walks of life, from CEO's to soccer moms. <span>For example, Piracetam was one of the first lab created compounds specifically designed to enhance cognitive performance, and although it is a synthesized chemical (with chemical name 2-oxo-1-pyrrolidine acetamide) it is generally regarded as being safe. The vast majority of people can take this supplement without needing to worry about suffering from any major side effects. However, there are also many notable natural and herbal nootro







Lion’s Mane – 500 mg, once per dayGingko Biloba – 240 mg, once per dayBacopa Monnieri – 100 mg, twice per day

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How to Make Your Own Smart Drugs
nent that you would take individually is not typically a wise choice due to the way that each supplement blends together. For this stack, most folks use the following daily combination, and you can find most of this stuff in bulk on Amazon. <span>Lion’s Mane – 500 mg, once per day Gingko Biloba – 240 mg, once per day Bacopa Monnieri – 100 mg, twice per day After 12 weeks, if you are not experiencing positive results, you may need to adjust the dosages in your stack. Start with small increments such as increasing each dose of the Bacopa




Flashcard 1486213483788

Question
Lion’s Mane – 500 mg, once per dayGingko Biloba – 240 mg, once per day[...] – 100 mg, twice per day
Answer
Bacopa Monnieri


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Lion’s Mane – 500 mg, once per dayGingko Biloba – 240 mg, once per dayBacopa Monnieri – 100 mg, twice per day

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How to Make Your Own Smart Drugs
nent that you would take individually is not typically a wise choice due to the way that each supplement blends together. For this stack, most folks use the following daily combination, and you can find most of this stuff in bulk on Amazon. <span>Lion’s Mane – 500 mg, once per day Gingko Biloba – 240 mg, once per day Bacopa Monnieri – 100 mg, twice per day After 12 weeks, if you are not experiencing positive results, you may need to adjust the dosages in your stack. Start with small increments such as increasing each dose of the Bacopa







Flashcard 1486214532364

Question
Amazon.
Answer
Amazon.


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How to Make Your Own Smart Drugs
each component that you would take individually is not typically a wise choice due to the way that each supplement blends together. For this stack, most folks use the following daily combination, and you can find most of this stuff in bulk on <span>Amazon. Lion’s Mane – 500 mg, once per day Gingko Biloba – 240 mg, once per day Bacopa Monnieri – 100 mg, twice per day After 12 weeks, if you are not experiencing positive results, you may







Flashcard 1486217415948

Tags
#deeplearning #neuralnetworks
Question
Other measures such as [...] normalize the con tribution of each v ariable in order to measure only how m uc h the v ariables are related, rather than also b eing affected b y the scale of the separate v ariables
Answer
correlation


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Other measures such as correlation normalize the con tribution of each v ariable in order to measure only how m uc h the v ariables are related, rather than also b eing affected b y the scale of the separate v ariables</s

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Flashcard 1486218988812

Tags
#deeplearning #neuralnetworks
Question
Other measures such as correlation [...] in order to measure only how m uc h the v ariables are related, rather than also b eing affected b y the scale of the separate v ariables
Answer
normalize the con tribution of each v ariable


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Other measures such as correlation normalize the con tribution of each v ariable in order to measure only how m uc h the v ariables are related, rather than also b eing affected b y the scale of the separate v ariables

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Flashcard 1486220561676

Tags
#deeplearning #neuralnetworks
Question
Other measures such as correlation normalize the con tribution of each v ariable in order to [...]
Answer
measure only how m uc h the v ariables are related, rather than also b eing affected b y the scale of the separate v ariables


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Other measures such as correlation normalize the con tribution of each v ariable in order to measure only how m uc h the v ariables are related, rather than also b eing affected b y the scale of the separate v ariables

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regional economic forum

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APEC consists of 21 member-economies

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Flashcard 1486269844748



Tags
#has-images
Question
Answer

Architettura a "bus", struttura più semplice...


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Flashcard 1486273252620



Tags
#has-images
Question

Answer
Memoria distribuita dove ogni processo ha accesso alla memoria..


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Flashcard 1486276660492



Tags
#has-images
Question

Answer
Viene utilizzato uno switch come un interrutore. Se un processo vuole accedere in memoria deve richiederlo allo switch.
Sarà compito dello switch smistarlo alla memoria richiesta. E' migliore della struttura a memoria distribuita, ma bisogna tenere presente il tempo di switch.


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Flashcard 1486280068364



Tags
#has-images
Question

Answer
Accesso a switch ma con collegamento diretto alla propria Ram.


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Flashcard 1486283476236



Tags
#has-images
Question


Questi modelli sono utilizzabili solo a patto che [...]

Answer
il tempo di switch sia basso


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Flashcard 1486285311244

Tags
#has-images
Question
Crossbar switch
Answer


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Flashcard 1486291078412



Tags
#has-images
Question
Rete Omega
Answer


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Flashcard 1486292389132



Tags
#has-images
Question
CrossPoint switch
Answer



Non possono essere chiusi due switch sulla stessa riga o colonna (come le torri negli scacchi)


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Flashcard 1486303137036



Tags
#has-images
Question
Mesh
Answer


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Flashcard 1486306544908



Tags
#has-images
Question
Hypercube
Answer


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Flashcard 1486383090956

Tags
#exam-fails #fra-introduction
Question
If a company's operating cycle lasts 2 years, which timeframe should be used to categorize current assets?
Answer
C. Two years


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Flashcard 1486384925964

Tags
#exam-fails #fra-introduction
Question
At the beginning of the year, Chock Company had $50,000 in assets and $20,000 in liabilities. At the end of the year, the company had $80,000 in assets and $40,000 in liabilities. If, during the year, no investments were made in the business and dividends of $2,000 were declared and paid during the year, net income for the year must have been ______.

A. $8,000

B. $10,000

C. $12,000
Answer
Correct Answer: C

Net income for the year was $12,000. Net income or loss (revenues less expenses) and dividends affect stockholders' equity. Stockholders' equity would have been $30,000 at the beginning of the year ($50,000 - $20,000) and $40,000 at the end of the year ($80,000 - $40,000). The change in stockholders' equity over the year was a $10,000 increase. If $2,000 was declared and paid in dividends, net income must have been $12,000 ($30,000 + $12,000 - $2,000 = $40,000).


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Flashcard 1486390955276

Tags
#deeplearning #neuralnetworks
Question
The [...] matrix of a random vector x ∈ R n is an n n × matrix, suc h that Co v( ) x i,j = Co v( x i , x j ) . (3.14) The diagonal elemen ts of the co v ariance give the v ariance: Co v( x i , x i ) = V ar( x i )
Answer
co v ariance


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The co v ariance matrix of a random vector x ∈ R n is an n n × matrix, suc h that Co v( ) x i,j = Co v( x i , x j ) . (3.14) The diagonal elemen ts of the co v ariance give the v ariance: Co v( x i , x

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Flashcard 1486393314572

Tags
#deeplearning #neuralnetworks
Question
The co v ariance matrix of a random vector x ∈ R n is an n n × matrix, suc h that [...] . (3.14) The diagonal elements of the covariance give the variance: Cov( x i , x i ) = Var( x i )
Answer
Cov(x)i,j = Cov( x i , x j )


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The co v ariance matrix of a random vector x ∈ R n is an n n × matrix, suc h that Cov(x) i,j = Cov( x i , x j ) . (3.14) The diagonal elements of the covariance give the variance: Cov( x i , x i ) = Var( x i )

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Flashcard 1486394887436

Tags
#deeplearning #neuralnetworks
Question
The co v ariance matrix of a random vector x ∈ R n is an n n × matrix, suc h that Cov(x)i,j = Cov( x i , x j ) . (3.14) The diagonal elements of the covariance give [...]
Answer
the variance: Cov( x i , x i ) = Var( x i )


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The co v ariance matrix of a random vector x ∈ R n is an n n × matrix, suc h that Cov(x) i,j = Cov( x i , x j ) . (3.14) The diagonal elements of the covariance give the variance: Cov( x i , x i ) = Var( x i )

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The Bernoulli distribution is [definition]. It is controlled by a single parameter φ ∈ [0 , 1] , whic h gives the probability of the random v ariable b eing equal to 1. It has the following prop erties: P φ ( = 1) = x (3.16) P φ ( = 0) = 1 x − (3.17) P x φ ( = x ) = x (1 ) − φ 1 − x (3.18) E x [ ] = x φ (3.19) V ar x ( ) = (1 ) x φ − φ
Answer
a distribution ov er a single binary random v ariable


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The Bernoulli distribution is a distribution ov er a single binary random v ariable. It is controlled by a single parameter φ ∈ [0 , 1] , whic h gives the probability of the random v ariable b eing equal to 1. It has the following prop erties: P φ ( = 1) = x (3.16) P

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The Bernoulli distribution is a distribution ov er a single binary random v ariable. It is controlled by a single parameter φ ∈ [0 , 1] , whic h gives the [...] It has the following prop erties: P φ ( = 1) = x (3.16) P φ ( = 0) = 1 x − (3.17) P x φ ( = x ) = x (1 ) − φ 1 − x (3.18) E x [ ] = x φ (3.19) V ar x ( ) = (1 ) x φ − φ
Answer
probability of the random v ariable b eing equal to 1.


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The Bernoulli distribution is a distribution ov er a single binary random v ariable. It is controlled by a single parameter φ ∈ [0 , 1] , whic h gives the probability of the random v ariable b eing equal to 1. It has the following prop erties: P φ ( = 1) = x (3.16) P φ ( = 0) = 1 x − (3.17) P x φ ( = x ) = x (1 ) − φ 1 − x (3.18) E x [ ] = x φ (3.19) V ar x ( ) = (1 ) x φ − φ</spa

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What is the expectation of the bionomial distribution?
Answer
E x [x] = φ


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is controlled by a single parameter φ ∈ [0 , 1] , whic h gives the probability of the random v ariable b eing equal to 1. It has the following prop erties: P (x = 1) = φ P (x = 0) = 1-φ P (x = x ) = φ x (1 − φ) 1 − x <span>E x [x] = φ V ar (x) = φ (1− φ)<span><body><html>

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What is the variance of the bionomial distribution?
Answer
V ar (x) = φ (1− φ)


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ingle parameter φ ∈ [0 , 1] , whic h gives the probability of the random v ariable b eing equal to 1. It has the following prop erties: P (x = 1) = φ P (x = 0) = 1-φ P (x = x ) = φ x (1 − φ) 1 − x E x [x] = φ <span>V ar (x) = φ (1− φ)<span><body><html>

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In some cases, we wish to [...] This can b e accomplished b y defining a PDF using the Dirac delta function, : δ x ( ) p x δ x µ . ( ) = ( − ) (3.27) The Dirac delta function is defined such that it is zero-v alued everywhere except 0, y et integrates to 1.
Answer
sp ecify that all of the mass in a probabilit y distribution clusters around a single p oin t.


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In some cases, we wish to sp ecify that all of the mass in a probabilit y distribution clusters around a single p oin t. This can b e accomplished b y defining a PDF using the Dirac delta function, : δ x ( ) p x δ x µ . ( ) = ( − ) (3.27) The Dirac delta function is defined such that it is zero-v alued eve

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In some cases, we wish to sp ecify that all of the mass in a probabilit y distribution clusters around a single p oin t. This can b e accomplished b y defining a PDF using the [...] function, : p(x) = δ(x-µ)
Answer
Dirac delta


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In some cases, we wish to sp ecify that all of the mass in a probabilit y distribution clusters around a single p oin t. This can b e accomplished b y defining a PDF using the Dirac delta function, : δ x ( ) p x δ x µ . ( ) = ( − ) (3.27) The Dirac delta function is defined such that it is zero-v alued everywhere except 0, y et integrates to 1.

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In some cases, we wish to sp ecify that all of the mass in a probabilit y distribution clusters around a single p oin t. This can b e accomplished b y defining a PDF using the Dirac delta function, : δ x ( ) p x δ x µ . ( ) = ( − ) (3.27) The Dirac delta function is defined such that [...]
Answer
it is zero-v alued everywhere except 0, y et integrates to 1.


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ll of the mass in a probabilit y distribution clusters around a single p oin t. This can b e accomplished b y defining a PDF using the Dirac delta function, : δ x ( ) p x δ x µ . ( ) = ( − ) (3.27) The Dirac delta function is defined such that <span>it is zero-v alued everywhere except 0, y et integrates to 1.<span><body><html>

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In some cases, we wish to sp ecify that all of the mass in a probabilit y distribution clusters around a single p oin t. This can b e accomplished b y defining a PDF using the Dirac delta function, : [...] The Dirac delta function is defined such that it is zero-v alued everywhere except 0, y et integrates to 1.
Answer
p(x) = δ(x-µ)


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In some cases, we wish to sp ecify that all of the mass in a probabilit y distribution clusters around a single p oin t. This can b e accomplished b y defining a PDF using the Dirac delta function, : δ(x) = δ(x-µ) The Dirac delta function is defined such that it is zero-v alued everywhere except 0, y et integrates to 1.

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mathematical ob ject called a [...] that is defined in terms of its prop erties when integrated
Answer
generalized function


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mathematical ob ject called a generalized function that is defined in terms of its prop erties when integrated

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Another imp ortant p ersp ective on the [...] distribution is that it is the probabilit y density that maximizes the likelihoo d of the training data
Answer
empirical


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Another imp ortant p ersp ective on the empirical distribution is that it is the probabilit y density that maximizes the likelihoo d of the training data

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Another imp ortant p ersp ective on the empirical distribution is that it is the probabilit y density that [...]
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maximizes the likelihoo d of the training data


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Another imp ortant p ersp ective on the empirical distribution is that it is the probabilit y density that maximizes the likelihoo d of the training data

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A [...] v ariable is a random v ariable that w e cannot observe directly .
Answer
latent


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A latent v ariable is a random v ariable that w e cannot observe directly .

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A latent v ariable is a [...]
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random v ariable that w e cannot observe directly .


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A latent v ariable is a random v ariable that w e cannot observe directly .

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Certain functions arise often while working with probability distributions, especially the probabilit y distributions used in deep learning models. One of these functions is the : logistic sigmoid [...]
Answer
σ(x) = 1/(1 + exp(− x))


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Certain functions arise often while working with probability distributions, especially the probabilit y distributions used in deep learning models. One of these functions is the : logistic sigmoid σ(x) = 1/(1 + exp(− x))

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Certain functions arise often while working with probability distributions, especially the probabilit y distributions used in deep learning models. One of these functions is the : [...] σ(x) = 1/(1 + exp(− x))
Answer
logistic sigmoid


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Certain functions arise often while working with probability distributions, especially the probabilit y distributions used in deep learning models. One of these functions is the : logistic sigmoid σ(x) = 1/(1 + exp(− x))

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[...] matrix, meaning it can control the v ariance separately along each axis-aligned direction.
Answer
diagonal co v ariance


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diagonal co v ariance matrix, meaning it can control the v ariance separately along each axis-aligned direction.

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diagonal co v ariance matrix, meaning it can [...]
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control the v ariance separately along each axis-aligned direction.


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diagonal co v ariance matrix, meaning it can control the v ariance separately along each axis-aligned direction.

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Another commonly encountered function is the softplus function ( , Dugas et al. 2001 ): [...] The softplus function can be useful for pro ducing the β or σ parameter of a normal distribution b ecause its range is (0 , ∞ )
Answer
ζ(x) = log (1 + exp(x))


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Another commonly encountered function is the softplus function ( , Dugas et al. 2001 ): ζ(x) = log (1 + exp(x)) The softplus function can be useful for pro ducing the β or σ parameter of a normal distribution b ecause its range is (0 , ∞ )

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Another commonly encountered function is the [...] ( , Dugas et al. 2001 ): ζ(x) = log (1 + exp(x))
Answer
softplus function


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Another commonly encountered function is the softplus function ( , Dugas et al. 2001 ): ζ(x) = log (1 + exp(x)) The softplus function can be useful for pro ducing the β or σ parameter of a normal distribution b ecause its range is (0 , ∞ )</

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Another commonly encountered function is the softplus function ( , Dugas et al. 2001 ): ζ(x) = log (1 + exp(x)) The softplus function can be useful for pro ducing the [...] parameter of a normal distribution b ecause its range is (0 , ∞ )
Answer
β or σ


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Another commonly encountered function is the softplus function ( , Dugas et al. 2001 ): ζ(x) = log (1 + exp(x)) The softplus function can be useful for pro ducing the β or σ parameter of a normal distribution b ecause its range is (0 , ∞ )

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Another commonly encountered function is the softplus function ( , Dugas et al. 2001 ): ζ(x) = log (1 + exp(x)) The softplus function can be useful for pro ducing the β or σ parameter of a normal distribution b ecause [...]
Answer
its range is (0 , ∞ )


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<head>Another commonly encountered function is the softplus function ( , Dugas et al. 2001 ): ζ(x) = log (1 + exp(x)) The softplus function can be useful for pro ducing the β or σ parameter of a normal distribution b ecause its range is (0 , ∞ )<html>

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Just as x can b e recov ered from its p ositive part and negativ e part via the iden tit y [...] , it is also p ossible to reco v er x using the same relationship b et w een and ζ (x), ζ ( −x)
Answer
x + − x = x


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Just as x can b e recov ered from its p ositive part and negativ e part via the iden tit y x + − x − = x , it is also p ossible to reco v er x using the same relationship b et w een and ζ (x), ζ ( −x)

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Just as x can b e recov ered from its p ositive part and negativ e part via the iden tit y x + − x = x , it is also p ossible to reco v er x using the same relationship b et w een and [...]
Answer
ζ (x), ζ ( −x)


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Just as x can b e recov ered from its p ositive part and negativ e part via the iden tit y x + − x − = x , it is also p ossible to reco v er x using the same relationship b et w een and ζ (x), ζ ( −x)

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Supp ose we ha v e t w o random v ariables, x and y , suc h that y = g ( x ) , where g is an inv ertible, con- tin uous, differen tiable transformation. One migh t exp ect that [...] This is actually not the case.
Answer
p y ( y ) = p x ( g − 1 ( y )) .


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Supp ose we ha v e t w o random v ariables, x and y , suc h that y = g ( x ) , where g is an inv ertible, con- tin uous, differen tiable transformation. One migh t exp ect that p y ( y ) = p x ( g − 1 ( y )) . This is actually not the case.

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Supp ose we ha v e t w o random v ariables, x and y , suc h that y = g ( x ) , where g is an inv ertible, con- tin uous, differen tiable transformation. One migh t exp ect that p y ( y ) = p x ( g − 1 ( y )) . This is [...]
Answer
actually not the case.


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d>Supp ose we ha v e t w o random v ariables, x and y , suc h that y = g ( x ) , where g is an inv ertible, con- tin uous, differen tiable transformation. One migh t exp ect that p y ( y ) = p x ( g − 1 ( y )) . This is actually not the case.<html>

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The basic intuition b ehind information theory is [...]
Answer
that learning that an unlik ely ev en t has occurred is more informative than learning that a lik ely ev ent has o ccurred


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The basic intuition b ehind information theory is that learning that an unlik ely ev en t has occurred is more informative than learning that a lik ely ev ent has o ccurred

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In order to satisfy all three of these prop erties, w e define the [...] of an event x = x to be I x = −log P(x)
Answer
self-information


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In order to satisfy all three of these prop erties, w e define the self-information of an event x = x to be I x = −log P(x)

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In order to satisfy all three of these prop erties, w e define the self-information of an event x = x to be [...]
Answer
I x = −log P(x)


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In order to satisfy all three of these prop erties, w e define the self-information of an event x = x to be I x = −log P(x)

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