Learn PyTorch — Quick Self-Study Guide

1) Introduction

This repository is a compact starting point for self-learning PyTorch. It outlines what PyTorch is, why it is useful for deep learning and experimentation, and how to navigate the included materials.

2) Installation

Overview of installing PyTorch with pip or conda for both CPU and GPU setups, plus a quick way to verify your environment. Choose the method that fits your platform and hardware.

3) PyTorch in 1 Minute

A high-level view of core concepts: tensors (GPU-ready arrays), autograd (automatic differentiation), and nn.Module (building blocks for neural networks).

4) Minimal Neural Network

A brief description of defining a small model, selecting a loss function and optimizer, and running a short training loop to update parameters.

5) Using GPU

How to move models and tensors to the same device (CPU or CUDA) and common pitfalls when mixing devices.

6) Suggested Learning Path

A practical progression: tensors and operations → autograd → building models with nn.Module → training and evaluation loops → data pipelines (Dataset, DataLoader) → saving/loading models.

7) Resources

Pointers to official documentation, tutorials, and the platform-specific installation guide to go deeper and stay up to date.

8) Tips and Common Pitfalls

Quick advice on avoiding device mismatch, handling gradient accumulation correctly, and ensuring reproducibility when needed.

9) Project Structure

Use this as a map of the repository content and how to use it.

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