PyTorch

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

PyTorch is an open-source machine-learning library that provides tensor computation and automatic differentiation for building and training neural networks.

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes

Demonstrates network definitions, tensor-based action selection, advantage calculation, optimization, tracking, and visualization.

Examples

Uses CartPole actors and critics, episode-reward plots, and Stable-Baselines3 training and evaluation scripts.

What it assumes

Assumes familiarity with reinforcement-learning environments, policies, values, rewards, and actor–critic terminology.

What you can do after

Builds and trains a reinforcement-learning agent with custom PyTorch networks or Stable-Baselines3.

How it connects

Connects neural networks with reinforcement learning, A2C, PPO, Gymnasium, categorical distributions, and Adam optimization.

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Also explained in

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  Inside Deep Learning: Math, Algorithms, ModelsA Python deep learning library that provides tensor computation, automatic differentiation, and data abstractions for neural network training.
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  Deep Learning with R, Third EditionPyTorch is an open-source machine-learning library providing tensor computation and automatic differentiation capabilities.
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  Deep Learning with PyTorch, Second EditionThe PyTorch core Python library namespace that exposes tensor operations and functions such as mathematical elementwise operations.
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  Natural Language Processing in Action, Second EditionPyTorch is an open-source machine-learning framework for building and training neural-network models with tensor computation and automatic differentiation.
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  Deep Learning with Python, Third EditionA Python machine-learning library used here as one of the frameworks whose code can run element-wise operations on GPUs; only PyTorch is actually a library entry in the glossary, but the section groups it with JAX and TensorFlow as frameworks.
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  Learn Generative AI with PyTorchA deep learning library used here for building and training neural networks, especially with dynamic computation graphs and GPU acceleration.
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  Math and Architectures of Deep LearningAn open-source machine learning library used here to provide the chapter's executable code examples and automatic gradient computation.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIPyTorch is an open-source machine learning framework for tensor computation and neural-network development.
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  Build a Large Language Model (From Scratch)The deep learning library used here to normalize attention scores along a specified tensor dimension with softmax.
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  Build a Reasoning Model (From Scratch)A deep learning library used here to represent tensors and define optimizers that minimize losses.

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Related concepts

• PyTorch Geometricused by · PyTorch Geometric is an open-source Python library that extends PyTorch with data structures and operators for graph and geometric machine learning.
• fastaiused by · fastai is a high-level deep learning library built on PyTorch that provides layered abstractions for model development.
• to methodhas part · The PyTorch to method moves a tensor or module to a specified device and can change its data type.
• GPU kerneluses · A GPU kernel is an executable program or function that performs computation on a graphics processing unit, commonly using parallel threads.

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.