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Modern PyTorch Guide home page

Official Docs
GitHub
GitHub

Building Models

Community
Forums

Getting Started

Overview
Study Plan 1
Study Plan 2

Tensors & Operations

Introduction to Tensors
Tensor Creation
Indexing & Slicing
Tensor Operations
Broadcasting
Views strides
Named tensors
Tensor attributes
Notes

Autograd & Differentiation

Autograd Basics
Computational Graph
Custom Autograd Functions
Gradients Explained
Requires grad
Forward mode

Neural Network Basics

nn.Module Basics
Parameters & Buffers
Forward Pass
Hooks
State dict
Init strategies

Data Loading

Datasets
DataLoaders
Transforms
Samplers
Pin memory
Webdataset

ETL Process

Overview

Autograd Basics

Autograd & Differentiation

Autograd Basics

Automatic differentiation fundamentals in PyTorch

Documentation Index
Fetch the complete documentation index at: https://newtorch.aboneda.com/llms.txt
Use this file to discover all available pages before exploring further.

Autograd Basics

requires_grad, backward(), grad, and the basics of automatic differentiation.

Notes Computational Graph

⌘I

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