How deeply does Deep Learning with Python, Third Edition cover deep learning framework?

Defines three core capabilities and gives historical context, but does not detail framework-specific APIs or implementation internals.

Deep learning framework

As explained in Deep Learning with Python, Third Edition

Software libraries for building and training neural network models.

A deep learning framework replaces hand-written low-level gradient code with reusable tools for building and training neural networks. Keras, JAX, TensorFlow, and PyTorch are prominent examples. section 3.1

A modern framework combines three capabilities:

Automatic differentiation computes gradients for arbitrary differentiable functions. section 3.1
Tensor computation runs on CPUs, GPUs, and potentially specialized deep learning hardware. section 3.1
Distributed execution spreads computation across multiple devices or computers. section 3.1

Why frameworks matter

Instead of writing gradient logic by hand in C++ and targeting one GPU with custom CUDA code, use a framework that packages differentiation, tensor execution, and distribution. section 3.1

How Deep Learning with Python, Third Edition covers it

Cover of Deep Learning with Python, Third Edition

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Also explained in

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Natural Language Processing in Action, Second EditionA deep learning framework is software that provides abstractions and computational support for constructing, training, and running neural-network models.
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Deep Learning with R, Third EditionA deep learning framework is a software system that provides reusable abstractions and computational capabilities for building and training neural networks.

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Related concepts

PyTorchincludes · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
Kerasincludes · Keras is an open-source, high-level deep-learning API for building, training, evaluating, and deploying neural-network models.
JAXincludes · JAX is a Python library for high-performance array-based numerical computing with composable transformations including automatic differentiation, compilation, vectorization, and parallelization.
TensorFlowincludes · TensorFlow is an open-source software platform for numerical computation and machine learning, with tools for developing, training, and deploying models.
automatic differentiationuses · Automatic differentiation is a computational technique that evaluates derivatives of a program by systematically applying the chain rule to its elementary operations.
Model Servingaddresses · A model-serving platform is software infrastructure that hosts trained models and exposes inference by accepting requests and returning model outputs.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with Python, Third Edition. The section links point to the parts of the book it draws on.