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on 25-Nov-2025 (Tue)

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Flashcard 7768438607116

Tags
#pytest #python #unittest
Question

Beware of float return values!
0.1 + 0.1 + 0.1 == 0.3 Sometimes false

assert 0.1 + 0.1 + 0.1 == 0.3, "Usual way to compare does not always work with floats!"

Instead use:

assert 0.1 + 0.1 + 0.1 == [...].approx(0.3)

Answer
pytest

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

Open it
are of float return values! 0.1 + 0.1 + 0.1 == 0.3 Sometimes false assert 0.1 + 0.1 + 0.1 == 0.3, "Usual way to compare does not always work with floats!" Instead use: assert 0.1 + 0.1 + 0.1 == <span>pytest.approx(0.3) <span>






Flashcard 7769171561740

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
In principle, one could evaluate the logistic regression model at every single time-step in the consumer history to determine the influence of individual events. However, this would involve the inefficient process of re-calculating features for every time-step. Calculations at timesteps t and t − 1 would be highly [...]: features at t represent the complete history until t and not only what happened in between t − 1 and t.
Answer
redundant

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

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ine the influence of individual events. However, this would involve the inefficient process of re-calculating features for every time-step. Calculations at timesteps t and t − 1 would be highly <span>redundant: features at t represent the complete history until t and not only what happened in between t − 1 and t. <span>

Original toplevel document (pdf)

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Flashcard 7772024474892

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
While training a model, the analyst aims at setting the parameters and hyperparameters such that the model reaches [...] capacity (Goodfellow et al., 2016) and therefore maximizes the chances that the model will generalize well to unseen data.
Answer
optimal

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
While training a model, the analyst aims at setting the parameters and hyperparameters such that the model reaches optimal capacity (Goodfellow et al., 2016) and therefore maximizes the chances that the model will generalize well to unseen data.

Original toplevel document (pdf)

cannot see any pdfs