Deep learning framework

Q: How deeply does Natural Language Processing in Action, Second Edition cover deep learning framework?

Explains backpropagation’s gradient-and-weight process, then compares framework APIs, GPU support, deployment suitability, and ecosystem direction.

As explained in Natural Language Processing in Action, Second Edition

A deep learning framework is software that provides abstractions and computational support for constructing, training, and running neural-network models.

A deep learning framework supplies abstractions and computational support for constructing, training, and running neural-network models. It automates backpropagation: calculating gradients and adjusting weights to reduce loss. section 5.2.4

Framework choice affects how models are developed and deployed:

PyTorch offers a flexible, intuitive, Pythonic interface and is designed for GPU-based parallel processing. section 5.2.4
TensorFlow remains used for large-scale deployments and supporting tools and hardware, while Keras makes TensorFlow more accessible. section 5.2.4

Comparing frameworks with data

A pandas workflow reads a Wikipedia comparison of deep learning software, converts capability fields into scores, and ranks frameworks by portability. The resulting list places PyTorch first, followed by Apache MXNet and TensorFlow.

How Natural Language Processing in Action, Second Edition covers it

Cover of Natural Language Processing in Action, Second Edition

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Also explained in

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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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Deep Learning with Python, Third EditionSoftware libraries for building and training neural network models.

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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 Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.