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Annotation 7658446916876

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
RNNs allow to link individual actions to predictions in a straightforward manner. To our knowledge, in this work we provide the first visualizations that exploit this in the context of e-commerce
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RNNs allow to link individual actions to predictions in a straightforward manner. To our knowledge, in this work we provide the first visualizations that exploit this in the context of e-commerce. This allows to quantify how predictions are affected by specific actions or action sequences conducted by the consumer. These insights are drawn on a quantitative empirical basis— in c

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Flashcard 7658448751884

Tags
#abm #agent-based #machine-learning #model #priority #synergistic-integration
Question
[...] learning falls in between supervised or unsupervised learning algorithms.
Answer
Semisupervised

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Semisupervised learning falls in between supervised or unsupervised learning algorithms. It is an approach that combines a small amount of labeled data with a large amount of unlabeled data during tra

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Annotation 7658450586892

#feature-engineering #lstm #recurrent-neural-networks #rnn
LSTM neural networks rely on raw unsummarized data to predict customer behaviors and can be scaled easily to very complex settings involving multiple streams of data
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ture engineering is not only a time- consuming process, it is also error-prone, complex, and highly dependent on the analyst's domain knowledge (or, sometimes, lack thereof). On the other hand, <span>LSTM neural networks rely on raw unsummarized data to predict customer behaviors and can be scaled easily to very complex settings involving multiple streams of data <span>

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Flashcard 7658452684044

Tags
#has-images #tensorflow #tensorflow-certificate
[unknown IMAGE 7626420784396]
Question

Saving and loading models

Two formats:

  • SavedModel format (including optimizer's step)
  • HDF5 format

What about TensorFlow Serving format?

# Save the entire model using SavedModel
model_3.save("best_model_3_SavedModel")
# SavedModel is in principle [...]) file # Save model in HDF5 format:
model_3.save("best_model_3_HDF5.h5")
Answer
protobuff

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rmat (including optimizer's step) HDF5 format What about TensorFlow Serving format? # Save the entire model using SavedModel model_3.save("best_model_3_SavedModel") # SavedModel is in principle <span>protobuff)pb file # Save model in HDF5 format: model_3.save("best_model_3_HDF5.h5") <span>

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TfC 01 regression
is to track the results of your experiments. There are tools to help us! Resource: Try: Tensorboard - a component of Tensorflow library to help track modelling experiments Weights & Biases <span>Saving and loading models Two formats: SavedModel format (including optimizer's step) HDF5 format What about TensorFlow Serving format? # Save the entire model using SavedModel model_3.save("best_model_3_SavedModel") # SavedModel is in principle protobuff)pb file # Save model in HDF5 format: model_3.save("best_model_3_HDF5.h5") Load model loaded_model_SM = tf.keras.models.load_model('/content/best_model_3_SavedModel') loaded_model_SM.summary() <span>