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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

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised 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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repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

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

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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. 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 7667369774348

Tags

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

Question

LSTMs may not be ideal for all sequence prediction problems. For example, in time series forecasting, often the information relevant for making a forecast is within a small window of past observations. Often an MLP with a [...] or a linear model may be a less complex and more suitable model

Answer

window

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

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ll sequence prediction problems. For example, in time series forecasting, often the information relevant for making a forecast is within a small window of past observations. Often an MLP with a window or a linear model may be a less complex and more suitable model

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Flashcard 7667371871500

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 time-invariant covariate (gender) and a time-varying, [...]-level covariate (marketing appeals)

Answer

individual

status	not learned	measured difficulty	37% [default]	last interval [days]
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xtual information which is shared across the individuals within a given cohort. In addition, in this example, we include also an individual time-invariant covariate (gender) and a time-varying, individual-level covariate (marketing appeals)

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Annotation 7667373706508

#deep-learning #embeddings

embedding technique allows us to represent product or user as low dimensional continuous vectors, while the one-hot encoding method will lead to the curse of dimensionality for the machine learning models

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n 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. This embedding technique allows us to represent product or user as low dimensional continuous vectors, while the one-hot encoding method will lead to the curse of dimensionality for the machine learning models

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Annotation 7667375803660

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

The LSTM input layer must be 3D

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The LSTM input layer must be 3D. The meaning of the 3 input dimensions are: samples, time steps and features

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Flashcard 7667377638668

Tags

#has-images #recurrent-neural-networks #rnn

[unknown IMAGE 7101511240972]

Question

To forecast future customer behavior, our model is trained using individual sequences of past transaction events, i.e., [...] accounts of a customer’s lifetime. The example in Table 2 describes one such customer’s transaction history over seven consecutive discrete time periods

Answer

chronological

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

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To forecast future customer behavior, our model is trained using individual sequences of past transaction events, i.e., chronological accounts of a customer’s lifetime. The example in Table 2 describes one such customer’s transaction history over seven consecutive discrete time periods

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Flashcard 7667378949388

Tags

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

Question

A time window based MLP outperformed the LSTM pure-[autoregression] approach on certain time series prediction benchmarks solvable by looking at a [...] inputs only.

Answer

few recent

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

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A time window based MLP outperformed the LSTM pure-[autoregression] approach on certain time series prediction benchmarks solvable by looking at a few recent inputs only.

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Flashcard 7667678842124

Question

366 367 368 369 380 382 383 • Question #179 • Question #180 • Question #181 • Question #182 • Question #183 • Question #184 • Question #185 Microsoft - MB-260 Practice Questions - SecExams.com SecExams - Focus Only on What's Needed to Pass! - [secexams.com] Question #1 You are a Customer Data Platform Specialist. You need to create relationships to connect entities so that they can be further used in defining segments and measures by the marketing team. Which three relationship types are available in audience insights? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Answer

[default - edit me]

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

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Flashcard 7667679890700

Question

[default - edit me]

Answer

A) Non-editable system relationships, created by the system as part of the data unification process (Correct Answer) B) Non-editable system relationships, which are created automatically from ingesting data sources C) Editable inherited relationships, created by the system as part of the data unification process D) Editable custom relationships, created and configured by users (Correct Answer) E) Non-editable inherited relationships, which are created automatically from ingesting data

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

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Annotation 7667684085004

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last reprioritisation on		suggested re-reading day
started reading on		finished reading on

Edited, memorised or added to reading queue

on 20-Nov-2024 (Wed)

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