pytorch-deep-learning: Learn PyTorch for Deep Learning from Zero to Mastery

pytorch-deep-learning: Learn PyTorch for Deep Learning from Zero to Mastery

Summary

This repository provides comprehensive materials for the "Learn PyTorch for Deep Learning: Zero to Mastery" course. It offers a hands-on, code-first approach to mastering PyTorch, covering fundamentals to advanced topics like computer vision and model deployment. With over 16,000 stars, it's a highly popular resource for beginners in machine learning and deep learning.

Repository Info

Updated on November 8, 2025
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pytorchdeep-learningmachine-learningjupyter-notebookcomputer-visionmodel-deploymenteducationtutorial

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Introduction

The pytorch-deep-learning repository by mrdbourke offers a comprehensive, hands-on course titled "Learn PyTorch for Deep Learning: Zero to Mastery." This highly-starred project provides all the materials needed to learn PyTorch from fundamentals to advanced applications. It emphasizes a code-first approach, encouraging learners to experiment and build practical deep learning models. The course content is regularly updated, including a recent tutorial for PyTorch 2.0.

Installation

Getting started with the pytorch-deep-learning course is straightforward, primarily utilizing Google Colab, a free resource for machine learning experimentation.

To begin:

  • Navigate to any course section, for example, "00 - PyTorch Fundamentals".
  • Click the "Open in Colab" button at the top of the page.
  • Execute the cells by pressing SHIFT+Enter to follow along and run the code.

Examples

The course covers a wide array of PyTorch applications through structured notebooks, each with exercises, extra-curriculum, and slides. Here are a few highlights:

  • 00 - PyTorch Fundamentals: Introduces core PyTorch operations essential for deep learning and neural networks.
  • 03 - PyTorch Computer Vision: Demonstrates how to apply PyTorch to computer vision problems using established workflows.
  • 06 - PyTorch Transfer Learning: Explores the powerful technique of adapting pre-trained models to new problems.
  • 09 - Milestone Project 3: Model Deployment: Guides learners through deploying a working PyTorch model to the internet, making it accessible to others.

All course materials are also available as a readable online book at learnpytorch.io.

Why Use This Repository?

This course is specifically designed for beginners in machine learning or deep learning who want to master PyTorch through practical application. It requires 3-6 months of Python coding experience and a basic understanding of machine learning concepts. The teaching style is apprenticeship-based, focusing on writing PyTorch code in Google Colab notebooks. By completing the course, you will have written hundreds of lines of PyTorch code, gained exposure to critical machine learning concepts, and built a portfolio of projects, including the overarching FoodVision project, a neural network for classifying food images. This hands-on experience prepares you to confidently tackle your own machine learning projects.

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