How deeply does Build a Large Language Model (From Scratch) cover PyTorch?

Identifies PyTorch as the implementation library but defers comprehensive library instruction elsewhere.

PyTorch

As explained in Build a Large Language Model (From Scratch)

The deep learning library used here to normalize attention scores along a specified tensor dimension with softmax.

PyTorch is the deep learning library used to implement large language models. It supports the hands-on construction of an LLM and provides a foundation for later pretraining or fine-tuning on domain-specific data. section 1.3

PyTorch fits into the LLM development workflow by supporting:

Building an LLM from the ground up to understand its mechanics and limitations. section 1.3
Pretraining on large, diverse text datasets before fine-tuning on narrower task- or domain-specific data. section 1.3
Developing custom models for specialized domains, privacy-sensitive data, local deployment, and developer-controlled updates. section 1.3

How Build a Large Language Model (From Scratch) covers it

Cover of Build a Large Language Model (From Scratch)

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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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Optimization Algorithms: AI techniques for design, planning, and control problemsPyTorch is an open-source machine-learning library that provides tensor computation and automatic differentiation for building and training neural networks.
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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 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 Build a Large Language Model (From Scratch). The section links point to the parts of the book it draws on.