How deeply does Distributed Machine Learning Patterns cover Keras?

Defines layers, compilation, fitting, evaluation, saving, and tuning, but does not descend into TensorFlow internals.

Keras

As explained in Distributed Machine Learning Patterns

Keras is a high-level deep-learning API for defining, training, evaluating, and exporting neural-network models, commonly used through TensorFlow as tf.keras.

Keras is TensorFlow’s high-level API for defining, training, evaluating, and saving neural-network models. It turns a prepared dataset into an executable model workflow. section 8.1.1

A typical Keras workflow:

Load MNIST through tf.keras.datasets and inspect its NumPy arrays. section 8.1.1
Normalize pixel values from 0–255 to 0–1. section 8.1.1
Define a Sequential network with Flatten, Dense, and Dropout layers. section 8.1.1
Compile with an optimizer, loss function, and accuracy metric, then fit and evaluate it. section 8.1.1
Save the trained model and reload it for later use. section 8.1.1

MNIST classifier

A compact model flattens 28×28 images, applies a 128-unit ReLU layer and dropout, then predicts ten digit classes with softmax. Five epochs reach about 97.9% test accuracy. section 8.1.1

Listing 8.8 The sequential model definition

model = tf.keras.models.Sequential([
 tf.keras.layers.Flatten(input_shape=(28, 28)),
 tf.keras.layers.Dense(128, activation='relu'),
 tf.keras.layers.Dropout(0.2),
 tf.keras.layers.Dense(10, activation='softmax')
])

Inspect the concrete Sequential architecture used for the image classifier. section 8.1.1

How Distributed Machine Learning Patterns covers it

Cover of Distributed Machine Learning Patterns

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Related concepts

TensorFlowuses · TensorFlow is an open-source software platform for numerical computation and machine learning, with tools for developing, training, and deploying models.
autoencoderused by · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.
deep learninguses · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
PyTorchuses · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
backenduses · A backend is a software component or subsystem that provides underlying processing, data access, execution, or services to clients or other components, typically behind a user-facing interface.
backpropagationimplements · Backpropagation is a procedure for computing derivatives of an output or loss with respect to inputs and parameters by applying the chain rule backward through a computational graph.

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