Edited, memorised or added to reading queue

on 05-Jan-2025 (Sun)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

Flashcard 7626541632780

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:

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

Answer
assert

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

Open it
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 == pytest.approx(0.3)






Flashcard 7628325260556

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
Recognizing relevant patterns in long input streams (> 100 actions) can turn out to be difficult for the human mind. To achieve better explainability, in many e-commerce applications consumer behavior can be viewed on the level of [...]
Answer
sessions

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
put streams (> 100 actions) can turn out to be difficult for the human mind. To achieve better explainability, in many e-commerce applications consumer behavior can be viewed on the level of <span>sessions <span>

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7673939102988

Tags
#feature-engineering #has-images #lstm #recurrent-neural-networks #rnn
[unknown IMAGE 7103892294924]
Question
Fig. 1. Four customers with markedly different purchase patterns but identical features in terms of recency (last purchase), frequency (number of purchases), and [...] (first purchase
Answer
seniority

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
Fig. 1. Four customers with markedly different purchase patterns but identical features in terms of recency (last purchase), frequency (number of purchases), and seniority (first purchase

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7673940937996

Tags
#bayes #programming #r #statistics
Question
The role of Bayesian inference is to compute the exact [...] credibilities of candidate parameter values, while also taking into account their prior probabilities.
Answer
relative

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
The role of Bayesian inference is to compute the exact relative credibilities of candidate parameter values, while also taking into account their prior probabilities.

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7673944083724

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
[...]-based machine learning methods like logistic regression take vectors f = (f 1 , . . . , f n ) of fixed length n as inputs. Applying these methods on consumer histories of arbitrary length requires feature engineering
Answer
Vector

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
Vector-based machine learning methods like logistic regression take vectors f = (f 1 , . . . , f n ) of fixed length n as inputs. Applying these methods on consumer histories of arbitrary leng

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7673945918732

Tags
#recurrent-neural-networks #rnn
Question
Other [ than ‘‘Buy ’Till You Die” (BTYD) ] options to capture changes between lower- and higher-frequency purchase episodes (as we observe for our customers in Fig. 1), or vice versa, are to adopt a dynamic [...] model (Fader, Hardie, & Chun-Yao, 2004), a simulation based model of the type presented by Rust, Kumar, and Venkatesan (2011), or to incorporate additional states other than the absorbing, inactive state as in standard BTYD latent attrition models.
Answer
changepoint

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
‘‘Buy ’Till You Die” (BTYD) ] options to capture changes between lower- and higher-frequency purchase episodes (as we observe for our customers in Fig. 1), or vice versa, are to adopt a dynamic <span>changepoint model (Fader, Hardie, & Chun-Yao, 2004), a simulation based model of the type presented by Rust, Kumar, and Venkatesan (2011), or to incorporate additional states other than the abs

Original toplevel document (pdf)

cannot see any pdfs






Flashcard 7673988648204

Tags
#tensorflow #tensorflow-certificate
Question

important:

This time there is a problem with loss function.

  • In case of categorical_crossentropy the labels have to be one-hot encoded

  • In case of labels as integeres use [...]

Answer
SparseCategoricalCrossentropy

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
important: This time there is a problem with loss function. In case of categorical_crossentropy the labels have to be one-hot encoded In case of labels as integeres use SparseCategoricalCrossentropy

Original toplevel document

TfC_02_classification-PART_2
y-axis -> true label x-axis -> predicted label # Create confusion metrics from sklearn.metrics import confusion_matrix y_preds = model_8.predict(X_test) confusion_matrix(y_test, y_preds) <span>important: This time there is a problem with loss function. In case of categorical_crossentropy the labels have to be one-hot encoded In case of labels as integeres use SparseCategoricalCrossentropy # Get the patterns of a layer in our network weights, biases = model_35.layers[1].get_weights() <span>