# on 18-May-2018 (Fri)

#### Annotation 1739219143948

#fields
In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers.

Field (mathematics) - Wikipedia
Module-like[show] Module Group with operators Vector space Linear algebra Algebra-like[show] Algebra Associative Non-associative Composition algebra Lie algebra Graded Bialgebra v t e <span>In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. The best known fields are the field of rational

#### Flashcard 1739221241100

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#fields
Question
In mathematics, a [...] is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers.
field

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In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers.

#### Original toplevel document

Field (mathematics) - Wikipedia
Module-like[show] Module Group with operators Vector space Linear algebra Algebra-like[show] Algebra Associative Non-associative Composition algebra Lie algebra Graded Bialgebra v t e <span>In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. The best known fields are the field of rational

#### Flashcard 1739223600396

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#fields
Question
In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to [...]

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In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers.

#### Original toplevel document

Field (mathematics) - Wikipedia
Module-like[show] Module Group with operators Vector space Linear algebra Algebra-like[show] Algebra Associative Non-associative Composition algebra Lie algebra Graded Bialgebra v t e <span>In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. The best known fields are the field of rational

#### Flashcard 1755486227724

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#optimization
Question

mathematical optimization selects a [...] (with regard to some criterion) from some set of available alternatives.

best element

there are many answers to this question, but this answer is more intuitive from the measure oriented perspective.

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mathematical optimization selects a best element (with regard to some criterion) from some set of available alternatives.

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Mathematical optimization - Wikipedia
+ 4. The global maximum at (x, y, z) = (0, 0, 4) is indicated by a blue dot. [imagelink] Nelder-Mead minimum search of Simionescu's function. Simplex vertices are ordered by their value, with 1 having the lowest (best) value. <span>In mathematics, computer science and operations research, mathematical optimization or mathematical programming, alternatively spelled optimisation, is the selection of a best element (with regard to some criterion) from some set of available alternatives.  In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the

#### Flashcard 1758223535372

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#fields
Question
In mathematics, a field is a set on which [...] are defined, and behave as when they are applied to rational and real numbers.

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In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers.

#### Original toplevel document

Field (mathematics) - Wikipedia
Module-like[show] Module Group with operators Vector space Linear algebra Algebra-like[show] Algebra Associative Non-associative Composition algebra Lie algebra Graded Bialgebra v t e <span>In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. The best known fields are the field of rational

#### Annotation 1758234021132

#fields
The best known fields are the field of rational numbers and the field of real numbers.

Field (mathematics) - Wikipedia
ltiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. <span>The best known fields are the field of rational numbers and the field of real numbers. The field of complex numbers is also widely used, not only in mathematics, but also in many areas of science and engineering. Many other fields, such as fields of rational functions, al

#### Flashcard 1758236118284

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#fields
Question
The best known fields are the field of [...] and [...] .

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The best known fields are the field of rational numbers and the field of real numbers.

#### Original toplevel document

Field (mathematics) - Wikipedia
ltiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. <span>The best known fields are the field of rational numbers and the field of real numbers. The field of complex numbers is also widely used, not only in mathematics, but also in many areas of science and engineering. Many other fields, such as fields of rational functions, al

#### Flashcard 1803451763980

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#jaynes #plausible-reasoning
Question
Plausible reasoning aims to develope [...] principless for inference
general, consistent, and unambiguous

People refuse to adopt? giant cunts!

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Plausible reasoning aims to develope general, consistent, and unambiguous principless for inference

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#### Flashcard 1804564303116

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#vim
Question
All find commands (search for character) can be followed [...] to go the the previous searched item

, (comma)

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All search commands can be followed , (comma) to go the the previous searched item

#### Original toplevel document

A Great Vim Cheat Sheet
er the cursor F [char] - Move to the next char on the current line before the cursor t [char] - Move to before the next char on the current line after the cursor T [char] - Move to before the next char on the current line before the cursor <span>All these commands can be followed by ; (semicolon) to go to the next searched item, and , (comma) to go the the previous searched item ##Insert/Appending/Editing Text Results in insert mode i - start insert mode at cursor I - insert at the beginning of the line a - append after the cursor A -

#### Flashcard 2961744465164

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#best-practice #stan
Question
One of the most powerful means of validating a statistical algorithm is to verify that you can [...].
recover the ground truth from simulated data

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One of the most powerful means of validating a statistical algorithm is to verify that you can recover the ground truth from simulated data.

#### Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
m used in Stan. All of these criteria are necessary but not sufficient conditions for a good fit -- in other words they all identify problems that will ensure a bad fit but none of them can guarantee a good fit. Recover simulated values <span>One of the most powerful means of validating a statistical algorithm is to verify that you can recover the ground truth from simulated data. Begin by selecting reasonable "true" values for each of your parameters, simulating data according to your model, and then trying to fit your model with the simulated data.

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#PATH
Question
the $PATH variable are usually set in[...files...] Answer shell.dotfiles ~/.zshrc or ~/.bashrc,or ~/.bash_profile status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it Well, first make sure your$PATH variable is doing what you want it to. You likely have a startup script called something like ~/.bash_profile or ~/.bashrc that sets this $PATH variable. #### Original toplevel document Running Jupyter with multiple Python and IPython paths - Stack Overflow s that the packages you can import when running python are entirely separate from the packages you can import when running ipython or a Jupyter notebook: you're using two completely independent Python installations. So how to fix this? <span>Well, first make sure your$PATH variable is doing what you want it to. You likely have a startup script called something like ~/.bash_profile or ~/.bashrc that sets this $PATH variable. On Windows, you can modify the user specific environment variables. You can manually modify that if you want your system to search things in a different order. When you first install an #### Flashcard 2965739539724 [unknown IMAGE 2965735083276] Tags #has-images #mapping Question a [...] function is a one-to-one and onto mapping of a set X to a set Y. Answer bijective status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it a bijective function a one-to-one and onto (surjective) mapping of a set X to a set Y. #### Original toplevel document Bijection - Wikipedia nction between the elements of two sets, where each element of one set is paired with exactly one element of the other set, and each element of the other set is paired with exactly one element of the first set. There are no unpaired elements. <span>In mathematical terms, a bijective function f: X → Y is a one-to-one (injective) and onto (surjective) mapping of a set X to a set Y. A bijection from the set X to the set Y has an inverse function from Y to X. If X and Y are finite sets, then the existence of a bijection means they have the same number of elements. #### Annotation 2965978615052 #sets #sigma-algebra the relative complement of A in B is the set of elements in B but not in A . The relative complement of $$B\cap A^{\complement }=B\setminus A$$ status not read #### Flashcard 2965984644364 [unknown IMAGE 2965987265804] Tags #has-images #sets #sigma-algebra Question the relative complement of A in B is the set of elements [...] . Answer in B but not in A $$B\cap A^{\complement }=B\setminus A$$ status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it the relative complement of A in B is the set of elements in B but not in A . The relative complement of $$B\cap A^{\complement }=B\setminus A$$ #### Original toplevel document Complement (set theory) - Wikipedia #### Annotation 2965990673676 #probability-theory If $${\mathcal {F}}\,$$ is the Borel σ-algebra on the set of real numbers, then there is a unique probability measure on $${\mathcal {F}}\,$$ for any cdf, and vice versa. status not read Probability theory - Wikipedia {\mathcal {F}}\,} is called a probability measure if P ( Ω ) = 1. P(\Omega )=1.\,} <span>If F {\mathcal {F}}\,} is the Borel σ-algebra on the set of real numbers, then there is a unique probability measure on F {\mathcal {F}}\,} for any cdf, and vice versa. The measure corresponding to a cdf is said to be induced by the cdf. This measure coincides with the pmf for discrete variables and pdf for continuous variables, making the measure-theo #### Flashcard 2965992770828 Tags #probability-theory Question If $${\mathcal {F}}\,$$ is [...], then there is a unique probability measure on $${\mathcal {F}}\,$$ for any cdf, and vice versa. Answer the Borel σ-algebra on the set of real numbers status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it If $${\mathcal {F}}\,$$ is the Borel σ-algebra on the set of real numbers, then there is a unique probability measure on $${\mathcal {F}}\,$$ for any cdf, and vice versa. #### Original toplevel document Probability theory - Wikipedia {\mathcal {F}}\,} is called a probability measure if P ( Ω ) = 1. P(\Omega )=1.\,} <span>If F {\mathcal {F}}\,} is the Borel σ-algebra on the set of real numbers, then there is a unique probability measure on F {\mathcal {F}}\,} for any cdf, and vice versa. The measure corresponding to a cdf is said to be induced by the cdf. This measure coincides with the pmf for discrete variables and pdf for continuous variables, making the measure-theo #### Annotation 2976163171596 #platão #timeu Segundo Platão/Sócrates, todo o ato intelectual só pode ter lugar na alma status not read #### pdf cannot see any pdfs #### Flashcard 2976165530892 Tags #platão #timeu Question Segundo Platão/Sócrates, todo o ato intelectual só pode ter lugar na [local] Answer alma status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it Segundo Platão/Sócrates, todo o ato intelectual só pode ter lugar na alma #### Original toplevel document (pdf) cannot see any pdfs #### Annotation 2976168152332 Tendo construído os corpos de cada um deles – sete ao todo –, o deus estabeleceu-os nas órbitas que o percurso do Outro seguia, em número de sete delas: na primeira a Lua, à volta da Terra; na segunda o Sol, por cima da Terra 125 ; a Estrela da Manhã 126 e o astro que dizem ser consagrado a Hermes 127 na rota circular 128 que tem a mesma velocidade que o Sol, ainda que lhes tenha cabido em sorte um ímpeto contrário ao dele status not read #### pdf cannot see any pdfs #### Annotation 2976169725196 Quanto à Terra, o nosso sustento, a qual roda 136 em torno do eixo que atravessa o universo, foi estabelecida como guardiã e produtora da noite e do dia; ela que é a primeira e a mais velha das divindades geradas dentro do céu status not read #### pdf cannot see any pdfs #### Annotation 2976171298060 De Geia e Urano foram gerados Oceano e Tétis, seus filhos, e destes foram gerados Fórcis, Cronos e Reia, e todos aqueles que os seguiram; de Cronos e de Reia foram gerados Zeus e Hera e todos aqueles que, segundo a tradição, sabemos serem seus irmãos, e ainda outros descendentes destes foram gerados. status not read #### pdf cannot see any pdfs #### Annotation 2976172870924 formai e engendrai seres-vivos, fazei-os crescer, providenciando- lhes o alimento, e, quando perecerem, recebei-os outra vez. status not read #### pdf cannot see any pdfs #### Annotation 2976174443788 Uma vez acalmados, gera-se o sossego, e, uma vez gerado um sossego profundo, abate-se um sono com poucos sonhos; mas quando restam alguns movimentos fortes, conforme a sua natureza e os locais onde ficam, produzem no interior simulacros que se assemelham, quanto à natureza e ao número, ao exterior e que serão recordados ao acordar. status not read #### pdf cannot see any pdfs #### Annotation 2976198036748 #best-practice #git You should only commit code when it’s completed. status not read Version Control Best Practices or everyone to integrate changes regularly and avoid having merge conflicts. Having few large commits and sharing them rarely, in contrast, makes it hard both to solve conflicts and to comprehend what happened. Don’t Commit Half-Done Work <span>You should only commit code when it’s completed. This doesn’t mean you have to complete a whole, large feature before committing. Quite the contrary: split the feature’s implementation into logical chunks and remember to commit early #### Annotation 2976199609612 #best-practice #git Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects status not read Version Control Best Practices ry before leaving the office at the end of the day. If you’re tempted to commit just because you need a clean working copy (to check out a branch, pull in changes, etc.) consider using Git’s “Stash” feature instead. Test Before You Commit <span>Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects (as far as one can tell). While committing half-baked things in your local repository only requires you to forgive yourself, having your code tested is even more important when it comes #### Annotation 2976201182476 #best-practice #git writing commit messages with the imperative, present tense to be consistent with generated messages from commands like git merge. status not read Version Control Best Practices . Separate it from the following body by including a blank line. The body of your message should provide detailed answers to the following questions: What was the motivation for the change? How does it differ from the previous implementation? <span>Use the imperative, present tense („change“, not „changed“ or „changes“) to be consistent with generated messages from commands like git merge. Version Control is not a Backup System Having your files backed up on a remote server is a nice side effect of having a version control system. But you should not use your VCS like #### Annotation 2976202755340 #best-practice #git The body of your commit message should provide motivation to and difference of the commit. status not read Version Control Best Practices n it comes to pushing / sharing your code with others. Write Good Commit Messages Begin your message with a short summary of your changes (up to 50 characters as a guideline). Separate it from the following body by including a blank line. <span>The body of your message should provide detailed answers to the following questions: What was the motivation for the change? How does it differ from the previous implementation? Use the imperative, present tense („change“, not „changed“ or „changes“) to be consistent with generated messages from commands like git merge. Version Control is not a Backup System #### Annotation 2976204328204 #best-practice #git Branches are the perfect tool to help you avoid mixing up different lines of development status not read Version Control Best Practices mitting semantically (see “related changes”) – you shouldn’t just cram in files. Use Branches Branching is one of Git’s most powerful features – and this is not by accident: quick and easy branching was a central requirement from day one. <span>Branches are the perfect tool to help you avoid mixing up different lines of development. You should use branches extensively in your development workflows: for new features, bug fixes, experiments, ideas… Agree on a Workflow Git lets you pick from a lot of different wo #### Annotation 2976205901068 #best-practice #git Branches status not read #### Parent (intermediate) annotation Open it Branches are the perfect tool to help you avoid mixing up different lines of development #### Original toplevel document Version Control Best Practices mitting semantically (see “related changes”) – you shouldn’t just cram in files. Use Branches Branching is one of Git’s most powerful features – and this is not by accident: quick and easy branching was a central requirement from day one. <span>Branches are the perfect tool to help you avoid mixing up different lines of development. You should use branches extensively in your development workflows: for new features, bug fixes, experiments, ideas… Agree on a Workflow Git lets you pick from a lot of different wo #### Flashcard 2976208260364 Tags #best-practice #git Question writing commit messages with the [...mood and tense...] to be consistent with generated messages from commands like git merge. Answer imperative, present status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it writing commit messages with the imperative, present tense to be consistent with generated messages from commands like git merge. #### Original toplevel document Version Control Best Practices . Separate it from the following body by including a blank line. The body of your message should provide detailed answers to the following questions: What was the motivation for the change? How does it differ from the previous implementation? <span>Use the imperative, present tense („change“, not „changed“ or „changes“) to be consistent with generated messages from commands like git merge. Version Control is not a Backup System Having your files backed up on a remote server is a nice side effect of having a version control system. But you should not use your VCS like #### Flashcard 2976210619660 Tags #best-practice #git Question The body of your commit message should provide the [... and ...] of the commit. Answer Why and what motivation and difference status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it The body of your commit message should provide motivation to and difference of the commit. #### Original toplevel document Version Control Best Practices n it comes to pushing / sharing your code with others. Write Good Commit Messages Begin your message with a short summary of your changes (up to 50 characters as a guideline). Separate it from the following body by including a blank line. <span>The body of your message should provide detailed answers to the following questions: What was the motivation for the change? How does it differ from the previous implementation? Use the imperative, present tense („change“, not „changed“ or „changes“) to be consistent with generated messages from commands like git merge. Version Control is not a Backup System #### Flashcard 2976212978956 Tags #best-practice #git Question Resist the temptation to commit something that [...]. Test it thoroughly to make sure it really is completed and has no side effects Answer you “think” is completed status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects #### Original toplevel document Version Control Best Practices ry before leaving the office at the end of the day. If you’re tempted to commit just because you need a clean working copy (to check out a branch, pull in changes, etc.) consider using Git’s “Stash” feature instead. Test Before You Commit <span>Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects (as far as one can tell). While committing half-baked things in your local repository only requires you to forgive yourself, having your code tested is even more important when it comes #### Flashcard 2976214551820 Tags #best-practice #git Question [...] before commit to make sure it is completed and has no side effects Answer Test it thoroughly status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects #### Original toplevel document Version Control Best Practices ry before leaving the office at the end of the day. If you’re tempted to commit just because you need a clean working copy (to check out a branch, pull in changes, etc.) consider using Git’s “Stash” feature instead. Test Before You Commit <span>Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects (as far as one can tell). While committing half-baked things in your local repository only requires you to forgive yourself, having your code tested is even more important when it comes #### Flashcard 2976216911116 Tags #best-practice #git Question You should only commit code when [...]. Answer it’s completed status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it You should only commit code when it’s completed. #### Original toplevel document Version Control Best Practices or everyone to integrate changes regularly and avoid having merge conflicts. Having few large commits and sharing them rarely, in contrast, makes it hard both to solve conflicts and to comprehend what happened. Don’t Commit Half-Done Work <span>You should only commit code when it’s completed. This doesn’t mean you have to complete a whole, large feature before committing. Quite the contrary: split the feature’s implementation into logical chunks and remember to commit early #### Annotation 2976224251148 #borel-algebra #measure-theory In contrast to Borel sets, an example of a non-measurable set cannot be exhibited, though its existence can be proved. status not read Borel set - Wikipedia ery probability measure on a standard Borel space turns it into a standard probability space. Non-Borel sets An example of a subset of the reals which is non-Borel, due to Lusin  (see Sect. 62, pages 76–78), is described below. <span>In contrast, an example of a non-measurable set cannot be exhibited, though its existence can be proved. Every irrational number has a unique representation by an infinite continued fraction x = a 0 #### Flashcard 2976226610444 Tags #borel-algebra #measure-theory Question In contrast to Borel sets, an example of a [...] cannot be exhibited, though its existence can be proved. status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it In contrast to Borel sets, an example of a non-measurable set cannot be exhibited, though its existence can be proved. #### Original toplevel document Borel set - Wikipedia ery probability measure on a standard Borel space turns it into a standard probability space. Non-Borel sets An example of a subset of the reals which is non-Borel, due to Lusin  (see Sect. 62, pages 76–78), is described below. <span>In contrast, an example of a non-measurable set cannot be exhibited, though its existence can be proved. Every irrational number has a unique representation by an infinite continued fraction x = a 0 #### Annotation 2976232115468 #probability-theory The raison d'être of the measure-theoretic treatment of probability is that it unifies the discrete and the continuous cases, and makes the difference a question of which measure is used. status not read Probability theory - Wikipedia R n \mathbb {R} ^{n}} and other continuous sample spaces. Measure-theoretic probability theory <span>The raison d'être of the measure-theoretic treatment of probability is that it unifies the discrete and the continuous cases, and makes the difference a question of which measure is used. Furthermore, it covers distributions that are neither discrete nor continuous nor mixtures of the two. An example of such distributions could be a mix of discrete and continuous distr #### Annotation 2976233688332 #probability-theory to study Brownian motion, probability is defined on a space of functions. status not read Probability theory - Wikipedia rk on probabilities outside R n \mathbb {R} ^{n}} , as in the theory of stochastic processes. For example, <span>to study Brownian motion, probability is defined on a space of functions. When it's convenient to work with a dominating measure, the Radon-Nikodym theorem is used to define a density as the Radon-Nikodym derivative of the probability distribution of intere #### Annotation 2976235261196 #probability-theory measure-theoretic treatment also allows us to work on probabilities outside $$\mathbb {R} ^{n}$$, as in the theory of stochastic processes . status not read Probability theory - Wikipedia \mu _{F}\,} induced by F . F\,.} Along with providing better understanding and unification of discrete and continuous probabilities, <span>measure-theoretic treatment also allows us to work on probabilities outside R n \mathbb {R} ^{n}} , as in the theory of stochastic processes. For example, to study Brownian motion, probability is defined on a space of functions. When it's convenient to work with a dominating measure, the Radon-Nikodym theorem is used to def #### Annotation 2976237358348 #probability-theory Weak convergence is also called convergence in distribution. Most common shorthand notation: $$\displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X$$ status not read Probability theory - Wikipedia F F\,} of X X\,} , wherever F F\,} is continuous. <span>Weak convergence is also called convergence in distribution. Most common shorthand notation: X n → D X \displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X} Convergence in probability The sequence of random variables X 1 , X #### Annotation 2976238931212 #probability-theory The sequence of random variables $$X_{1},X_{2},\dots \,$$ is said to converge towards the random variable $$X\,$$ in probability if $$\lim _{n\rightarrow \infty }P\left(\left|X_{n}-X\right|\geq \varepsilon \right)=0$$ for every ε > 0. Most common shorthand notation: $$\displaystyle X_{n}\,{\xrightarrow {P}}\,X$$ status not read Probability theory - Wikipedia D X \displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X} Convergence in probability <span>The sequence of random variables X 1 , X 2 , … X_{1},X_{2},\dots \,} is said to converge towards the random variable X X\,} in probability if lim n → ∞ P ( | X n − X | ≥ ε ) = 0 \lim _{n\rightarrow \infty }P\left(\left|X_{n}-X\right|\geq \varepsilon \right)=0} for every ε > 0. Most common shorthand notation: X n → P X \displaystyle X_{n}\,{\xrightarrow {P}}\,X} Strong convergence The sequence of random variables X 1 , X #### Annotation 2976240504076 #probability-theory The sequence of random variables $$X_{1},X_{2},\dots \,$$ is said to converge towards the random variable $$X\,$$ strongly if $$P(\lim _{n\rightarrow \infty }X_{n}=X)=1$$. Strong convergence is also known as almost sure convergence. Most common shorthand notation: $$\displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X$$ status not read Probability theory - Wikipedia → P X \displaystyle X_{n}\,{\xrightarrow {P}}\,X} Strong convergence <span>The sequence of random variables X 1 , X 2 , … X_{1},X_{2},\dots \,} is said to converge towards the random variable X X\,} strongly if P ( lim n → ∞ X n = X ) = 1 P(\lim _{n\rightarrow \infty }X_{n}=X)=1} . Strong convergence is also known as almost sure convergence. Most common shorthand notation: X n → a . s . X \displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X} As the names indicate, weak convergence is weaker than strong convergence. In fact, strong convergence implies convergence in probability, and convergence in probability implies w #### Flashcard 2976242076940 Tags #probability-theory Question Let $$(\Omega ,{\mathcal {F}},P)$$ be a probability space and $$(E,{\mathcal {E}})$$ a measurable space. Then [...] is a measurable function $$X\colon \Omega \to E$$ Answer an $$(E,{\mathcal {E}})$$-valued random variable status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it Let $$(\Omega ,{\mathcal {F}},P)$$ be a probability space and $$(E,{\mathcal {E}})$$ a measurable space. Then an $$(E,{\mathcal {E}})$$-valued random variable is a measurable function $$X\colon \Omega \to E$$, which means that, for every subset $$B\in {\mathcal {E}}$$, its preimage $$X^{-1}(B)\in {\mathcal {F}}$$ where $$X^{-1}(B)=\{\omega :X #### Original toplevel document Random variable - Wikipedia fined over any sets that can be derived either directly from continuous intervals of numbers or by a finite or countably infinite number of unions and/or intersections of such intervals.  The measure-theoretic definition is as follows. <span>Let ( Ω , F , P ) (\Omega ,{\mathcal {F}},P)} be a probability space and ( E , E ) (E,{\mathcal {E}})} a measurable space. Then an ( E , E ) (E,{\mathcal {E}})} -valued random variable is a measurable function X : Ω → E X\colon \Omega \to E} , which means that, for every subset B ∈ E B\in {\mathcal {E}}} , its preimage X − 1 ( B ) ∈ F X^{-1}(B)\in {\mathcal {F}}} where X − 1 ( B ) = { ω : X ( ω ) ∈ B } X^{-1}(B)=\{\omega :X(\omega )\in B\}} .  This definition enables us to measure any subset B ∈ E B\in {\mathcal {E}}} in the target space by looking at its preimage, which by assumption is measurable. In more intuitive terms, a member of Ω \Omega } is a possible outcome, a member of #### Flashcard 2976244436236 Tags #probability-theory Question Let \((\Omega ,{\mathcal {F}},P$$ be a probability space and $$(E,{\mathcal {E}})$$ a measurable space. Then an $$(E,{\mathcal {E}})$$-valued random variable is a [...] Answer measurable function $$X\colon \Omega \to E$$ Although real valued random variables are common, there can also be other kinds of variables status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it Let $$(\Omega ,{\mathcal {F}},P)$$ be a probability space and $$(E,{\mathcal {E}})$$ a measurable space. Then an $$(E,{\mathcal {E}})$$-valued random variable is a measurable function $$X\colon \Omega \to E$$, which means that, for every subset $$B\in {\mathcal {E}}$$, its preimage $$X^{-1}(B)\in {\mathcal {F}}$$ where $$X^{-1}(B)=\{\omega :X(\omega )\in B\}$$.  This definition enables u #### Original toplevel document Random variable - Wikipedia fined over any sets that can be derived either directly from continuous intervals of numbers or by a finite or countably infinite number of unions and/or intersections of such intervals.  The measure-theoretic definition is as follows. <span>Let ( Ω , F , P ) (\Omega ,{\mathcal {F}},P)} be a probability space and ( E , E ) (E,{\mathcal {E}})} a measurable space. Then an ( E , E ) (E,{\mathcal {E}})} -valued random variable is a measurable function X : Ω → E X\colon \Omega \to E} , which means that, for every subset B ∈ E B\in {\mathcal {E}}} , its preimage X − 1 ( B ) ∈ F X^{-1}(B)\in {\mathcal {F}}} where X − 1 ( B ) = { ω : X ( ω ) ∈ B } X^{-1}(B)=\{\omega :X(\omega )\in B\}} .  This definition enables us to measure any subset B ∈ E B\in {\mathcal {E}}} in the target space by looking at its preimage, which by assumption is measurable. In more intuitive terms, a member of Ω \Omega } is a possible outcome, a member of #### Flashcard 2976246795532 Tags #probability-theory Question Let $$(\Omega ,{\mathcal {F}},P)$$ be a probability space and $$(E,{\mathcal {E}})$$ a measurable space. Then an $$(E,{\mathcal {E}})$$-valued random variable means that [... description ...]. Answer every subset $$B\in {\mathcal {E}}$$ has a preimage $$X^{-1}(B)\in {\mathcal {F}}$$ Pretty much like any measurable function, just with a probability measure. where $$X^{-1}(B)=\{\omega :X(\omega )\in B\}$$ status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it dy>Let $$(\Omega ,{\mathcal {F}},P)$$ be a probability space and $$(E,{\mathcal {E}})$$ a measurable space. Then an $$(E,{\mathcal {E}})$$-valued random variable is a measurable function $$X\colon \Omega \to E$$, which means that, for every subset $$B\in {\mathcal {E}}$$, its preimage $$X^{-1}(B)\in {\mathcal {F}}$$ where $$X^{-1}(B)=\{\omega :X(\omega )\in B\}$$.  This definition enables us to measure any subset $$B\in {\mathcal {E}}$$ in the target space by looking at its preimage, which by assumption is measurable. <body>< #### Original toplevel document Random variable - Wikipedia fined over any sets that can be derived either directly from continuous intervals of numbers or by a finite or countably infinite number of unions and/or intersections of such intervals.  The measure-theoretic definition is as follows. <span>Let ( Ω , F , P ) (\Omega ,{\mathcal {F}},P)} be a probability space and ( E , E ) (E,{\mathcal {E}})} a measurable space. Then an ( E , E ) (E,{\mathcal {E}})} -valued random variable is a measurable function X : Ω → E X\colon \Omega \to E} , which means that, for every subset B ∈ E B\in {\mathcal {E}}} , its preimage X − 1 ( B ) ∈ F X^{-1}(B)\in {\mathcal {F}}} where X − 1 ( B ) = { ω : X ( ω ) ∈ B } X^{-1}(B)=\{\omega :X(\omega )\in B\}} .  This definition enables us to measure any subset B ∈ E B\in {\mathcal {E}}} in the target space by looking at its preimage, which by assumption is measurable. In more intuitive terms, a member of Ω \Omega } is a possible outcome, a member of #### Flashcard 2976249154828 Tags #probability-theory Question Weak convergence is also called [...]. Most common shorthand notation: $$\displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X$$ Answer convergence in distribution by distribution we means the CDF (cumulative distribution function) status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it Weak convergence is also called convergence in distribution. Most common shorthand notation: $$\displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X$$ #### Original toplevel document Probability theory - Wikipedia F F\,} of X X\,} , wherever F F\,} is continuous. <span>Weak convergence is also called convergence in distribution. Most common shorthand notation: X n → D X \displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X} Convergence in probability The sequence of random variables X 1 , X #### Flashcard 2976251514124 Tags #probability-theory Question convergence in probability means that [...]. Most common shorthand notation: $$\displaystyle X_{n}\,{\xrightarrow {P}}\,X$$ Answer $$\lim _{n\rightarrow \infty }P\left(\left|X_{n}-X\right|\geq \varepsilon \right)=0$$ for every ε > 0 status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it The sequence of random variables $$X_{1},X_{2},\dots \,$$ is said to converge towards the random variable $$X\,$$ in probability if $$\lim _{n\rightarrow \infty }P\left(\left|X_{n}-X\right|\geq \varepsilon \right)=0$$ for every ε > 0. Most common shorthand notation: $$\displaystyle X_{n}\,{\xrightarrow {P}}\,X$$ #### Original toplevel document Probability theory - Wikipedia D X \displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X} Convergence in probability <span>The sequence of random variables X 1 , X 2 , … X_{1},X_{2},\dots \,} is said to converge towards the random variable X X\,} in probability if lim n → ∞ P ( | X n − X | ≥ ε ) = 0 \lim _{n\rightarrow \infty }P\left(\left|X_{n}-X\right|\geq \varepsilon \right)=0} for every ε > 0. Most common shorthand notation: X n → P X \displaystyle X_{n}\,{\xrightarrow {P}}\,X} Strong convergence The sequence of random variables X 1 , X #### Flashcard 2976253873420 Tags #probability-theory Question strong convergence means that [...]. Most common shorthand notation: $$\displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X$$ Answer $$P(\lim _{n\rightarrow \infty }X_{n}=X)=1$$ status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it The sequence of random variables $$X_{1},X_{2},\dots \,$$ is said to converge towards the random variable $$X\,$$ strongly if $$P(\lim _{n\rightarrow \infty }X_{n}=X)=1$$. Strong convergence is also known as almost sure convergence. Most common shorthand notation: $$\displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X$$ #### Original toplevel document Probability theory - Wikipedia → P X \displaystyle X_{n}\,{\xrightarrow {P}}\,X} Strong convergence <span>The sequence of random variables X 1 , X 2 , … X_{1},X_{2},\dots \,} is said to converge towards the random variable X X\,} strongly if P ( lim n → ∞ X n = X ) = 1 P(\lim _{n\rightarrow \infty }X_{n}=X)=1} . Strong convergence is also known as almost sure convergence. Most common shorthand notation: X n → a . s . X \displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X} As the names indicate, weak convergence is weaker than strong convergence. In fact, strong convergence implies convergence in probability, and convergence in probability implies w #### Flashcard 2976256232716 Tags #probability-theory Question Strong convergence is also known as [...]. Answer almost sure convergence status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it tml>The sequence of random variables $$X_{1},X_{2},\dots \,$$ is said to converge towards the random variable $$X\,$$ strongly if $$P(\lim _{n\rightarrow \infty }X_{n}=X)=1$$. Strong convergence is also known as almost sure convergence. Most common shorthand notation: $$\displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X$$ <html> #### Original toplevel document Probability theory - Wikipedia → P X \displaystyle X_{n}\,{\xrightarrow {P}}\,X} Strong convergence <span>The sequence of random variables X 1 , X 2 , … X_{1},X_{2},\dots \,} is said to converge towards the random variable X X\,} strongly if P ( lim n → ∞ X n = X ) = 1 P(\lim _{n\rightarrow \infty }X_{n}=X)=1} . Strong convergence is also known as almost sure convergence. Most common shorthand notation: X n → a . s . X \displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X} As the names indicate, weak convergence is weaker than strong convergence. In fact, strong convergence implies convergence in probability, and convergence in probability implies w #### Flashcard 2976258592012 Tags #probability-theory Question to study Brownian motion, probability is defined on [...]. Answer a space of functions status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it to study Brownian motion, probability is defined on a space of functions. #### Original toplevel document Probability theory - Wikipedia rk on probabilities outside R n \mathbb {R} ^{n}} , as in the theory of stochastic processes. For example, <span>to study Brownian motion, probability is defined on a space of functions. When it's convenient to work with a dominating measure, the Radon-Nikodym theorem is used to define a density as the Radon-Nikodym derivative of the probability distribution of intere #### Flashcard 2976260164876 Tags #probability-theory Question measure-theoretic treatment also allows us to work on [...], as in the theory of stochastic processes . Answer probabilities outside $$\mathbb {R} ^{n}$$ status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it measure-theoretic treatment also allows us to work on probabilities outside $$\mathbb {R} ^{n}$$, as in the theory of stochastic processes . #### Original toplevel document Probability theory - Wikipedia \mu _{F}\,} induced by F . F\,.} Along with providing better understanding and unification of discrete and continuous probabilities, <span>measure-theoretic treatment also allows us to work on probabilities outside R n \mathbb {R} ^{n}} , as in the theory of stochastic processes. For example, to study Brownian motion, probability is defined on a space of functions. When it's convenient to work with a dominating measure, the Radon-Nikodym theorem is used to def #### Flashcard 2976261737740 Tags #probability-theory Question The raison d'être of the measure-theoretic treatment of probability is that it [...], and makes the difference a question of which measure is used. Answer unifies the discrete and the continuous cases status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it The raison d'être of the measure-theoretic treatment of probability is that it unifies the discrete and the continuous cases, and makes the difference a question of which measure is used. #### Original toplevel document Probability theory - Wikipedia R n \mathbb {R} ^{n}} and other continuous sample spaces. Measure-theoretic probability theory <span>The raison d'être of the measure-theoretic treatment of probability is that it unifies the discrete and the continuous cases, and makes the difference a question of which measure is used. Furthermore, it covers distributions that are neither discrete nor continuous nor mixtures of the two. An example of such distributions could be a mix of discrete and continuous distr #### Annotation 2976271961356 #vim To check, run vim --version and see if +clipboard exists. status not read A Great Vim Cheat Sheet You should now be able to press [space]w in normal mode to save a file. [space]p should paste from the system clipboard (outside of Vim). If you can’t paste, it’s probably because Vim was not built with the system clipboard option. <span>To check, run vim --version and see if +clipboard exists. If it says -clipboard , you will not be able to copy from outside of Vim. For Mac users, homebrew install Vim with the clipboard option. Install homebrew and then run brew install vim #### Flashcard 2976273534220 Tags #vim Question To check system clipboard compatibility, run [...] and see if +clipboard exists. Answer vim --version status measured difficulty not learned 37% [default] 0 #### Parent (intermediate) annotation Open it To check, run vim --version and see if +clipboard exists. #### Original toplevel document A Great Vim Cheat Sheet You should now be able to press [space]w in normal mode to save a file. [space]p should paste from the system clipboard (outside of Vim). If you can’t paste, it’s probably because Vim was not built with the system clipboard option. <span>To check, run vim --version and see if +clipboard exists. If it says -clipboard , you will not be able to copy from outside of Vim. For Mac users, homebrew install Vim with the clipboard option. Install homebrew and then run brew install vim #### Annotation 2976277204236 #shell echo$SHELL # displays the shell you're using

Bash CheatSheet for UNIX Systems --&gt; UPDATED VERSION --&gt; https://github.com/LeCoupa/awesome-cheatsheets · GitHub
in the foreground or bg in the background DELETE # deletes one character backward !! # repeats the last command exit # logs out of current session # 1. Bash Basics. export # displays all environment variables <span>echo $SHELL # displays the shell you're using echo$BASH_VERSION # displays bash version bash # if you want to use bash (type exit to go back to your normal shell) whereis bash # finds out where bash is on

#### Flashcard 2976278777100

Tags
#shell
Question
[...] # displays the shell you're using

#### Original toplevel document

Bash CheatSheet for UNIX Systems --&gt; UPDATED VERSION --&gt; https://github.com/LeCoupa/awesome-cheatsheets · GitHub
in the foreground or bg in the background DELETE # deletes one character backward !! # repeats the last command exit # logs out of current session # 1. Bash Basics. export # displays all environment variables <span>echo $SHELL # displays the shell you're using echo$BASH_VERSION # displays bash version bash # if you want to use bash (type exit to go back to your normal shell) whereis bash # finds out where bash is on

#### Annotation 2976281660684

#best-practice
Treat the data (and its format) as immutable.

n—if you've got thoughts, please contribute or share them. Data is immutable Don't ever edit your raw data, especially not manually, and especially not in Excel. Don't overwrite your raw data. Don't save multiple versions of the raw data. <span>Treat the data (and its format) as immutable. The code you write should move the raw data through a pipeline to your final analysis. You shouldn't have to run all of the steps every time you want to make a new figure (see Analysis

#### Annotation 2976283233548

#best-practice
Therefore, by default, the data folder is included in the .gitignore file.

new figure (see Analysis is a DAG), but anyone should be able to reproduce the final products with only the code in src and the data in data/raw . Also, if data is immutable, it doesn't need source control in the same way that code does. <span>Therefore, by default, the data folder is included in the .gitignore file. If you have a small amount of data that rarely changes, you may want to include the data in the repository. Github currently warns if files are over 50MB and rejects files over 100MB.

#### Annotation 2976284806412

#best-practice
When we use notebooks in our work, we often subdivide the notebooks folder.

unication Notebook packages like the Jupyter notebook, Beaker notebook, Zeppelin, and other literate programming tools are very effective for exploratory data analysis. However, these tools can be less effective for reproducing an analysis. <span>When we use notebooks in our work, we often subdivide the notebooks folder. For example, notebooks/exploratory contains initial explorations, whereas notebooks/reports is more polished work that can be exported as html to the reports directory. Since no

#### Annotation 2976286379276

#best-practice
Notebooks are for exploration and communication

for storing/syncing large data include AWS S3 with a syncing tool (e.g., s3cmd ), Git Large File Storage, Git Annex, and dat. Currently by default, we ask for an S3 bucket and use AWS CLI to sync data in the data folder with the server. <span>Notebooks are for exploration and communication Notebook packages like the Jupyter notebook, Beaker notebook, Zeppelin, and other literate programming tools are very effective for exploratory data analysis. However, these tools can

#### Annotation 2976287952140

#best-practice
Follow a naming convention that shows the owner and the order the analysis was done in.

control (e.g., diffs of the json are often not human-readable and merging is near impossible), we recommended not collaborating directly with others on Jupyter notebooks. There are two steps we recommend for using notebooks effectively: <span>Follow a naming convention that shows the owner and the order the analysis was done in. We use the format --.ipynb (e.g., 0.3-bull-visualize-distributions.ipynb ). Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data pr

#### Annotation 2976289525004

#best-practice
1. Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim. If it's useful utility code, refactor it to src.

. There are two steps we recommend for using notebooks effectively: Follow a naming convention that shows the owner and the order the analysis was done in. We use the format --.ipynb (e.g., 0.3-bull-visualize-distributions.ipynb ). <span>Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim . If it's useful utility code, refactor it to src . Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following: # OPTIONAL: Lo

#### Annotation 2976291097868

#best-practice
Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following:

rts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim . If it's useful utility code, refactor it to src . <span>Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following: # OPTIONAL: Load the "autoreload" extension so that code can change %load_ext autoreload # OPTIONAL: always reload modules so that as you change code in src, it gets loaded

#### Annotation 2976292670732

#best-practice
We prefer make for managing steps that depend on each other, especially the long-running ones.

an analysis you have long-running steps that preprocess data or train models. If these steps have been run already (and you have stored the output somewhere like the data/interim directory), you don't want to wait to rerun them every time. <span>We prefer make for managing steps that depend on each other, especially the long-running ones. Make is a common tool on Unix-based platforms (and is available for Windows). Following the make documentation, Makefile conventions, and portability guide will help ensure your Makef

#### Annotation 2976294243596

#best-practice
The first step in reproducing an analysis is always reproducing the computational environment it was run in.

re other tools for managing DAGs that are written in Python instead of a DSL (e.g., Paver, Luigi, Airflow, Snakemake, Ruffus, or Joblib). Feel free to use these if they are more appropriate for your analysis. Build from the environment up <span>The first step in reproducing an analysis is always reproducing the computational environment it was run in. You need the same tools, the same libraries, and the same versions to make everything play nicely together. One effective approach to this is use virtualenv (we recommend virtualenvwr

#### Annotation 2976295816460

#best-practice
By listing all of your requirements in the repository (we include a requirements.txt file) you can easily track the packages needed to recreate the analysis.

vironment it was run in. You need the same tools, the same libraries, and the same versions to make everything play nicely together. One effective approach to this is use virtualenv (we recommend virtualenvwrapper for managing virtualenvs). <span>By listing all of your requirements in the repository (we include a requirements.txt file) you can easily track the packages needed to recreate the analysis. Here is a good workflow: Run mkvirtualenv when creating a new project pip install the packages that your analysis needs Run pip freeze > requirements.txt to pin the exact pack

#best-practice

#### Store your secrets and config variables in a special file

Create a .env file in the project root folder. Thanks to the .gitignore, this file should never get committed into the version control repository.

p secrets and configuration out of version control You really don't want to leak your AWS secret key or Postgres username and password on Github. Enough said — see the Twelve Factor App principles on this point. Here's one way to do this: <span>Store your secrets and config variables in a special file Create a .env file in the project root folder. Thanks to the .gitignore , this file should never get committed into the version control repository. Here's an example: # example .env file DATABASE_URL=postgres://username:password@localhost:5432/dbname AWS_ACCESS_KEY=myaccesskey AWS_SECRET_ACCESS_KEY=mysecretkey OTHER_VARIABLE=some

#### Annotation 2976308399372

but setting the default editor and then using

 git commit - e 

might be much more comfortable.

bash - Add line break to 'git commit -m' from the command line - Stack Overflow
up vote 16 down vote I hope this isn't leading too far away from the posted question, <span>but setting the default editor and then using git commit -e might be much more comfortable. share|edit|flag edited Dec 22 '17 at 6:21

#### Flashcard 2976311020812

Tags
#git
Question

use [...] to write commit messages with default editor.

 git commit - e 

after setting the default editor,

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

Open it
but setting the default editor and then using git commit - e might be much more comfortable.

#### Original toplevel document

bash - Add line break to 'git commit -m' from the command line - Stack Overflow
up vote 16 down vote I hope this isn't leading too far away from the posted question, <span>but setting the default editor and then using git commit -e might be much more comfortable. share|edit|flag edited Dec 22 '17 at 6:21

#### Annotation 2976316263692

#best-practice
Providing metadata is a fundamental requirement when publishing data on the Web because data publishers and data consumers may be unknown to each other.

Data on the Web Best Practices
to tasks where metadata are used, for example, discovery and reuse. Best Practice 1: Provide metadata Provide metadata for both human users and computer applications. Why <span>Providing metadata is a fundamental requirement when publishing data on the Web because data publishers and data consumers may be unknown to each other. Then, it is essential to provide information that helps human users and computer applications to understand the data as well as other important aspects that describes a dataset or a dis

#### Flashcard 2976318360844

Tags
#best-practice
Question
[...] is a fundamental requirement when publishing data on the Web because data publishers and data consumers may be unknown to each other.

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

Open it
Providing metadata is a fundamental requirement when publishing data on the Web because data publishers and data consumers may be unknown to each other.

#### Original toplevel document

Data on the Web Best Practices
to tasks where metadata are used, for example, discovery and reuse. Best Practice 1: Provide metadata Provide metadata for both human users and computer applications. Why <span>Providing metadata is a fundamental requirement when publishing data on the Web because data publishers and data consumers may be unknown to each other. Then, it is essential to provide information that helps human users and computer applications to understand the data as well as other important aspects that describes a dataset or a dis

Tags
#best-practice
Question

#### Store your [...] in a special .env file in the project root folder, which is included in .gitignore

secret tokens and config variables

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

Open it
Store your secrets and config variables in a special file Create a .env file in the project root folder. Thanks to the .gitignore , this file should never get committed into the version control repository.

#### Original toplevel document

p secrets and configuration out of version control You really don't want to leak your AWS secret key or Postgres username and password on Github. Enough said — see the Twelve Factor App principles on this point. Here's one way to do this: <span>Store your secrets and config variables in a special file Create a .env file in the project root folder. Thanks to the .gitignore , this file should never get committed into the version control repository. Here's an example: # example .env file DATABASE_URL=postgres://username:password@localhost:5432/dbname AWS_ACCESS_KEY=myaccesskey AWS_SECRET_ACCESS_KEY=mysecretkey OTHER_VARIABLE=some

Tags
#best-practice
Question

#### Store your secret tokens and config variablesin a special .env file in the [...] folder, which is included in .gitignore

project root

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

Open it
Store your secrets and config variables in a special file Create a .env file in the project root folder. Thanks to the .gitignore , this file should never get committed into the version control repository.

#### Original toplevel document

p secrets and configuration out of version control You really don't want to leak your AWS secret key or Postgres username and password on Github. Enough said — see the Twelve Factor App principles on this point. Here's one way to do this: <span>Store your secrets and config variables in a special file Create a .env file in the project root folder. Thanks to the .gitignore , this file should never get committed into the version control repository. Here's an example: # example .env file DATABASE_URL=postgres://username:password@localhost:5432/dbname AWS_ACCESS_KEY=myaccesskey AWS_SECRET_ACCESS_KEY=mysecretkey OTHER_VARIABLE=some

#### Flashcard 2976324128012

Tags
#best-practice
Question
listing all dependences in a requirements.txt file so you can easily [...].
track the packages needed to recreate the analysis

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

Open it
By listing all of your requirements in the repository (we include a requirements.txt file) you can easily track the packages needed to recreate the analysis.

#### Original toplevel document

vironment it was run in. You need the same tools, the same libraries, and the same versions to make everything play nicely together. One effective approach to this is use virtualenv (we recommend virtualenvwrapper for managing virtualenvs). <span>By listing all of your requirements in the repository (we include a requirements.txt file) you can easily track the packages needed to recreate the analysis. Here is a good workflow: Run mkvirtualenv when creating a new project pip install the packages that your analysis needs Run pip freeze > requirements.txt to pin the exact pack

#### Flashcard 2976326487308

Tags
#best-practice
Question
The first step in reproducing an analysis is always reproducing [...].
the computational environment it was run in

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

Open it
The first step in reproducing an analysis is always reproducing the computational environment it was run in.

#### Original toplevel document

re other tools for managing DAGs that are written in Python instead of a DSL (e.g., Paver, Luigi, Airflow, Snakemake, Ruffus, or Joblib). Feel free to use these if they are more appropriate for your analysis. Build from the environment up <span>The first step in reproducing an analysis is always reproducing the computational environment it was run in. You need the same tools, the same libraries, and the same versions to make everything play nicely together. One effective approach to this is use virtualenv (we recommend virtualenvwr

#### Flashcard 2976328060172

Tags
#best-practice
Question
We prefer [...] for managing steps that depend on each other, especially the long-running ones.

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

Open it
We prefer make for managing steps that depend on each other, especially the long-running ones.

#### Original toplevel document

an analysis you have long-running steps that preprocess data or train models. If these steps have been run already (and you have stored the output somewhere like the data/interim directory), you don't want to wait to rerun them every time. <span>We prefer make for managing steps that depend on each other, especially the long-running ones. Make is a common tool on Unix-based platforms (and is available for Windows). Following the make documentation, Makefile conventions, and portability guide will help ensure your Makef

#### Flashcard 2976329633036

Tags
#best-practice
Question
we turn the project into a Python package by adding the [...] file.
setup.py

So you can import your code and use it in notebooks and any other places

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

Open it
Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following:

#### Original toplevel document

rts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim . If it's useful utility code, refactor it to src . <span>Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following: # OPTIONAL: Load the "autoreload" extension so that code can change %load_ext autoreload # OPTIONAL: always reload modules so that as you change code in src, it gets loaded

#### Flashcard 2976331992332

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#best-practice
Question

[...] so codes can be reused in multiple notebooks.

refactor to src.

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Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim . If it's useful utility code, refactor it to src . <span><body><html>

#### Original toplevel document

. There are two steps we recommend for using notebooks effectively: Follow a naming convention that shows the owner and the order the analysis was done in. We use the format --.ipynb (e.g., 0.3-bull-visualize-distributions.ipynb ). <span>Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim . If it's useful utility code, refactor it to src . Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following: # OPTIONAL: Lo

#### Flashcard 2976334351628

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#best-practice
Question
Follow a naming convention that shows at least [..., ..., and ...].
order, owner, and purpose

The other OOP

status measured difficulty not learned 37% [default] 0

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Follow a naming convention that shows the owner and the order the analysis was done in.

#### Original toplevel document

control (e.g., diffs of the json are often not human-readable and merging is near impossible), we recommended not collaborating directly with others on Jupyter notebooks. There are two steps we recommend for using notebooks effectively: <span>Follow a naming convention that shows the owner and the order the analysis was done in. We use the format --.ipynb (e.g., 0.3-bull-visualize-distributions.ipynb ). Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data pr

#### Flashcard 2976336710924

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#best-practice
Question
When we use notebooks in our work, we often [...].
subdivide the notebooks folder

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When we use notebooks in our work, we often subdivide the notebooks folder.

#### Original toplevel document

unication Notebook packages like the Jupyter notebook, Beaker notebook, Zeppelin, and other literate programming tools are very effective for exploratory data analysis. However, these tools can be less effective for reproducing an analysis. <span>When we use notebooks in our work, we often subdivide the notebooks folder. For example, notebooks/exploratory contains initial explorations, whereas notebooks/reports is more polished work that can be exported as html to the reports directory. Since no

#### Flashcard 2976338283788

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#best-practice
Question
Notebooks are for [...]
exploration and communication

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Notebooks are for exploration and communication

#### Original toplevel document

for storing/syncing large data include AWS S3 with a syncing tool (e.g., s3cmd ), Git Large File Storage, Git Annex, and dat. Currently by default, we ask for an S3 bucket and use AWS CLI to sync data in the data folder with the server. <span>Notebooks are for exploration and communication Notebook packages like the Jupyter notebook, Beaker notebook, Zeppelin, and other literate programming tools are very effective for exploratory data analysis. However, these tools can

#### Flashcard 2976339856652

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#best-practice
Question
Therefore, by default, the data folder is [...].
included in the .gitignore file

i.e. not in version control

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Therefore, by default, the data folder is included in the .gitignore file.

#### Original toplevel document

new figure (see Analysis is a DAG), but anyone should be able to reproduce the final products with only the code in src and the data in data/raw . Also, if data is immutable, it doesn't need source control in the same way that code does. <span>Therefore, by default, the data folder is included in the .gitignore file. If you have a small amount of data that rarely changes, you may want to include the data in the repository. Github currently warns if files are over 50MB and rejects files over 100MB.

#### Flashcard 2976342215948

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#best-practice
Question
Treat the raw data (and its format) as [...].
immutable

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Treat the data (and its format) as immutable.

#### Original toplevel document

n—if you've got thoughts, please contribute or share them. Data is immutable Don't ever edit your raw data, especially not manually, and especially not in Excel. Don't overwrite your raw data. Don't save multiple versions of the raw data. <span>Treat the data (and its format) as immutable. The code you write should move the raw data through a pipeline to your final analysis. You shouldn't have to run all of the steps every time you want to make a new figure (see Analysis

#### Annotation 2976344575244

#Make
Besides building programs, Make can be used to manage any project where some files must be updated automatically from others whenever the others change.

Make (software) - Wikipedia
y how to derive the target program. Though integrated development environments and language-specific compiler features can also be used to manage a build process, Make remains widely used, especially in Unix and Unix-like operating systems. <span>Besides building programs, Make can be used to manage any project where some files must be updated automatically from others whenever the others change. Contents [hide] 1 Origin 2 Derivatives 3 Behavior 4 Makefile 4.1 Rules 4.2 Macros 4.3 Suffix rules 4.4 Pattern rules 4.5 Other elements 5 Example makefiles 6 See also 7 R

#### Annotation 2976346148108

#Make

Make is invoked with a list of target file names to build as command-line arguments:

Make (software) - Wikipedia
urce) and the transformation actions might be to convert the file to some specific format, copy the result into a content management system, and then send e-mail to a predefined set of users indicating that the above actions were performed. <span>Make is invoked with a list of target file names to build as command-line arguments: make [TARGET ...] Without arguments, Make builds the first target that appears in its makefile, which is traditionally a symbolic "phony" target named all. Make d

#### Annotation 2976347720972

#Make
Make decides whether a target needs to be regenerated by comparing file modification times.

Make (software) - Wikipedia
a list of target file names to build as command-line arguments: make [TARGET ...] Without arguments, Make builds the first target that appears in its makefile, which is traditionally a symbolic "phony" target named all. <span>Make decides whether a target needs to be regenerated by comparing file modification times.  This solves the problem of avoiding the building of files which are already up to date, but it fails when a file changes but its modification time stays in the past. Such changes

#### Annotation 2976349293836

#Make
This solves the problem of avoiding the building of files which are already up to date, but it fails when a file changes but its modification time stays in the past.

Make (software) - Wikipedia
ut arguments, Make builds the first target that appears in its makefile, which is traditionally a symbolic "phony" target named all. Make decides whether a target needs to be regenerated by comparing file modification times.  <span>This solves the problem of avoiding the building of files which are already up to date, but it fails when a file changes but its modification time stays in the past. Such changes could be caused by restoring an older version of a source file, or when a network filesystem is a source of files and its clock or timezone is not synchronized with the mac

#### Annotation 2976351390988

#Make
Make searches the current directory for the makefile to use

Make (software) - Wikipedia
The user must handle this situation by forcing a complete build. Conversely, if a source file's modification time is in the future, it triggers unnecessary rebuilding, which may inconvenience users. Makefile Main article: Makefile <span>Make searches the current directory for the makefile to use, e.g. GNU make searches files in order for a file named one of GNUmakefile, makefile, Makefile and then runs the specified (or default) target(s) from (only) that file. The makefile

#### Annotation 2976352963852

#Make
The makefile language is similar to declarative programming. This class of language, in which necessary end conditions are described but the order in which actions are to be taken is not important

Make (software) - Wikipedia
akefile Make searches the current directory for the makefile to use, e.g. GNU make searches files in order for a file named one of GNUmakefile, makefile, Makefile and then runs the specified (or default) target(s) from (only) that file. <span>The makefile language is similar to declarative programming.     This class of language, in which necessary end conditions are described but the order in which actions are to be taken is not important, is sometimes confusing to programmers used to imperative programming. One problem in build automation is the tailoring of a build process to a given platform. For instance, the compi

#### Annotation 2976360828172

Make allows us to specify what depends on what and how to update things that are out of date.