How deeply does Machine Learning Algorithms in Depth cover dropout?

Defines random connection removal and contrasts dropout with weight decay and early stopping, but gives no mathematical derivation.

Dropout

As explained in Machine Learning Algorithms in Depth

Dropout is a regularization technique that randomly removes or zeroes neural-network connections or activations during training.

Dropout is a regularization technique for reducing overfitting in multilayer perceptrons. During training, dense connections are randomly dropped or zeroed according to a fixed rate, encouraging better generalization. section 10.1

Use dropout alongside other capacity and regularization choices:

Increase layers or hidden units when the model underfits. section 10.1
Use weight decay or L2 regularization to encourage smaller nonbias weights. section 10.1
Use early stopping when validation loss begins increasing away from training loss. section 10.1

Keras/TensorFlow MLP

A multilayer perceptron implementation imports Keras and TensorFlow, includes the Dense and Dropout layers, and applies the model to reshaped, normalized MNIST images.

How Machine Learning Algorithms in Depth covers it

Cover of Machine Learning Algorithms in Depth

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Deep Learning with Python, Third EditionA neural-network regularization technique that randomly zeroes activation entries during training and behaves differently at inference time.
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Deep Learning with PyTorch, Second EditionA regularization technique that randomly omits activations during training and is turned off or altered for inference.
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Natural Language Processing in Action, Second EditionA dropout layer is a neural-network layer that randomly disables units during training to reduce reliance on particular internal representations.
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LLMs in ProductionDropout is a regularization technique that randomly disables neural-network units during training to reduce co-adaptation and overfitting.
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Distributed Machine Learning PatternsA neural-network layer that randomly omits units during training to reduce overfitting and model complexity.

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Related concepts

overfittingaddresses · Overfitting is a modeling condition in which a model fits patterns or idiosyncrasies in its training data too closely and consequently generalizes poorly to new data.
regularizationkind of · Regularization is a modeling technique that constrains or penalizes solutions during fitting or optimization to discourage undesirable complexity or behavior and improve generalization or estimation.
AlexNetused by · AlexNet is a convolutional neural network architecture designed for image classification and recognized for its landmark performance in large-scale image recognition.
evaluation modeconfigured by · Evaluation mode is a model-execution state that changes or disables training-specific behaviors for evaluation or inference.
hidden layeruses · A hidden layer is an internal neural-network layer between the input and output layers that transforms intermediate representations.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.