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Flashcard 7642950536460

Tags
#bayesian #stan
Question
The Stan development crew has made it easy to interactively explore diagnostics via the shinystan package, and one should do so with each model. In addition, there are other diagnostics available in other packages like loo and [...].
Answer
posterior

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

Stan - diagnostic packages
e it easy to interactively explore diagnostics via the shinystan package, and one should do so with each model. In addition, there are other diagnostics available in other packages like loo and <span>posterior. <span>






Flashcard 7642951585036

Tags
#bayesian #stan
Question
The Stan development crew has made it easy to interactively explore diagnostics via the shinystan package, and one should do so with [...] model. In addition, there are other diagnostics available in other packages like loo and posterior.
Answer
each

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

Stan - diagnostic packages
The Stan development crew has made it easy to interactively explore diagnostics via the shinystan package, and one should do so with each model. In addition, there are other diagnostics available in other packages like loo and posterior.






Flashcard 7642952895756

Tags
#R #debugger #shiny
Question
Unlike [...], browser() works everywhere, so it’s suitable for use in any code invoked by your Shiny app.
Answer
breakpoints

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Unlike breakpoints, browser() works everywhere, so it’s suitable for use in any code invoked by your Shiny app.

Original toplevel document

Debuging shiny applications
The browser() statement is another useful debugging tool. It acts like a breakpoint–when evaluated, it halts execution and enters the debugger. You can add it anywhere an R expression is valid. <span>Unlike breakpoints, browser() works everywhere, so it’s suitable for use in any code invoked by your Shiny app. You can also invoke browser() conditionally to create conditional breakpoints; for instance: if (input$bins > 50) browser() The downside of browser() is that you need to re-run your






Flashcard 7642954206476

Tags
#deep-learning
Question
I'll explain a type of artificial neuron called a perceptron. Perceptrons were developed in the 1950s and 1960s by the scientist [...] inspired by earlier work by Warren McCulloch and Walter Pitts.

statusnot learnedmeasured difficulty37% [default]last interval [days]               
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Open it
I'll explain a type of artificial neuron called a perceptron. Perceptrons were developed in the 1950s and 1960s by the scientist Frank Rosenblatt, inspired by earlier work by Warren McCulloch and Walter Pitts.

Original toplevel document

Neural networks and deep learning
you'll attain. Amongst the payoffs, by the end of the chapter we'll be in position to understand what deep learning is, and why it matters. Perceptrons What is a neural network? To get started, <span>I'll explain a type of artificial neuron called a perceptron. Perceptrons were developed in the 1950s and 1960s by the scientist Frank Rosenblatt, inspired by earlier work by Warren McCulloch and Walter Pitts. Today, it's more common to use other models of artificial neurons - in this book, and in much modern work on neural networks, the main neuron model used is one called the sigmoid neuron






Annotation 7642956041484

#deep-learning #keras #lstm #python #sequence

The choice of time steps [number] will influence both:

- The internal state accumulated during the forward pass.

- The gradient estimate used to update weights on the backward pass.
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Careful choice must be given to the number of time steps specified when preparing your input data for sequence prediction problems in Keras. The choice of time steps will influence both: - The internal state accumulated during the forward pass. - The gradient estimate used to update weights on the backward pass. Note that by default, the internal state of the network is reset after each batch, but more explicit control over when the internal state is reset can be achieved by using a so-called s

Original toplevel document (pdf)

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Flashcard 7642958925068

Tags
#has-images #recurrent-neural-networks #rnn
[unknown IMAGE 7101511240972]
Question
The two calendar components – the month and week indicators – represent time-varying contextual information which is shared across the individuals within a given cohort. In addition, in this example, we include also an individual [...] covariate (gender) and a time-varying, individual-level covariate (marketing appeals)
Answer
time-invariant

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nth and week indicators – represent time-varying contextual information which is shared across the individuals within a given cohort. In addition, in this example, we include also an individual <span>time-invariant covariate (gender) and a time-varying, individual-level covariate (marketing appeals) <span>

Original toplevel document (pdf)

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Flashcard 7642960760076

Tags
#ML-engineering #ML_in_Action #learning #machine #software-engineering
Question
ML engineers need to know just enough software development skills to be able to write [...] code and implement unit tests. They don’t need to know about the intricacies of non-blocking asynchronous messaging brokering.
Answer
modular

statusnot learnedmeasured difficulty37% [default]last interval [days]               
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ML engineers need to know just enough software development skills to be able to write modular code and implement unit tests. They don’t need to know about the intricacies of non-blocking asynchronous messaging brokering.

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7642962595084

Tags
#deep-learning #embeddings
Question
With the similar idea of how we get word embeddings, we can make an analogy like this: a word is like a [...]; a sentence is like a sequence of ONE customer’s shopping sequence; an article is like a sequence of ALL customers’ shopping sequence
Answer
product

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

Parent (intermediate) annotation

Open it
With the similar idea of how we get word embeddings, we can make an analogy like this: a word is like a product; a sentence is like a sequence of ONE customer’s shopping sequence; an article is like a sequence of ALL customers’ shopping sequence

Original toplevel document (pdf)

cannot see any pdfs