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Primary: Phase 3Documentation / TutorialsIntermediateFree Learning Material
PyTorch

PyTorch Beginner Tutorials

Official 'Learn the Basics' tutorial series covering PyTorch Tensors, Datasets/DataLoaders, `nn.Module`, `torch.autograd`, optimization loops, and model checkpointing.

After building your own autograd engine and NumPy neural network in Modules 3.1–3.3, Module 3.4 transitions you to PyTorch. The official 'Learn the Basics' 7-part tutorial walks through every core PyTorch primitive: Tensors, Datasets & DataLoaders, Transforms, Building Neural Networks (`nn.Module`), Automatic Differentiation (`torch.autograd`), Optimizing Model Parameters, and Saving/Loading state dicts.

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Suggested Learning Position

Phase 3 Module 3.4 Core Tutorial (PyTorch Fundamentals)

Language:English
Access & Link Verification

100% free official PyTorch documentation with downloadable Jupyter notebooks.

Status: Verified (2026-11-09)

What You Will Learn from This Resource

  • Manipulate PyTorch tensors across CPU and GPU devices.
  • Construct custom `Dataset`, `DataLoader`, and `nn.Module` classes.
  • Write complete `zero_grad() -> backward() -> step()` training loops and save `state_dict` checkpoints.

Recommended Prerequisites

Review these foundations before starting so you get the most out of this material:

  • Python, NumPy arrays, and understanding of gradients/backpropagation (Modules 3.1–3.3).

Topics & Keywords

PyTorchTensorsAutogradnn.ModuleDataLoaderTraining Loop
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Phase-Specific Study Recommendations (1)

Phase 3 · Resource 3.3Practice & Reference

PyTorch Beginner Tutorials

In Phase 3: Deep Learning & Neural Networks

Required hands-on tutorial for Module 3.4 (PyTorch Fundamentals). Complete all 7 sections of 'Learn the Basics'.

Jump to Phase 3 (Deep Learning & Neural Networks)

Directly Associated Roadmap Modules (1)

Module 3.4Phase 3

Other Deep Learning Architectures

Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, Autoencoders, Generative Models, and Reinforcement Learning fundamentals.

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