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Annotation 7674648726796

[unknown IMAGE 7101515435276] #has-images #recurrent-neural-networks #rnn
Fig. 2. The structure of the model begins with its input layers for (i) the input variable (i.e., transaction counts) and (ii) optional covariates (time-invariant or time-varying inputs)
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A schematic high-level representation of the proposed model architecture is shown in Fig. 2. The structure of the model begins with its input layers for (i) the input variable (i.e., transaction counts) and (ii) optional covariates (time-invariant or time-varying inputs). These variable inputs enter the model through dedicated input layers at the top of the model’s architecture and are combined by simply concatenating them into a single long vector. Thi

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Annotation 7674650561804

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
A session is a well-defined visit of a consumer to a web- shop: a subsequence of events within the consumer’s history that lay no further apart than a predefined time difference
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To achieve better explainability, in many e-commerce applications consumer behavior can be viewed on the level of sessions. A session is a well-defined visit of a consumer to a web- shop: a subsequence of events within the consumer’s history that lay no further apart than a predefined time difference.

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Flashcard 7674652134668

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#has-images #tensorflow #tensorflow-certificate
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Question

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  • tf.keras.losses.[...]()
  • combintion of MSE and MAE less sensitive to outliers than MSE
Answer
Huber

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Huber tf.keras.losses.Huber() combintion of MSE and MAE less sensitive to outliers than MSE

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TfC 01 regression
st_labels, c="green", label="Testing data") plt.scatter(test_data, predictions, c="red", label="Predictions") plt.legend(); Common regression evaluation metrics keyboard_arrow_down Introduction <span>For regression problems: MAE tf.keras.losses.MAE() tf.metrics.mean_absolute_error() great starter metrics for any regression problem MSE tf.keras.losses.MSE() tf.metrics.mean_square_error() when larger errors are more significant that smaller errors Huber tf.keras.losses.Huber() combintion of MSE and MAE less sensitive to outliers than MSE Take away: You should minimize the time between your experiments (that's way you should start with smaller models). The more experiments you do, the more things you figure out that don't work. Tracking your experiments One really good habit is to track the results of your experiments. There are tools to help us! Resource: Try: Tensorboard - a component of Tensorflow library t