mrdbourke/pytorch-deep-learning
mrdbourke/pytorch-deep-learning is an AI-powered project on GitHub with 18.6k stars, written primarily in Jupyter Notebook. Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
Snapshot summary built from the project's own GitHub metadata — there's no written TopGit review yet. The page will update automatically when a full review is published.
TopGit writes full reviews for the most-starred, most-requested repositories. This page is a snapshot until then — see the READ ME tab for the original README in full.
Snapshot
Top contributors
Show top contributors
Learn PyTorch for Deep Learning
Welcome to the Zero to Mastery Learn PyTorch for Deep Learning course, the second best place to learn PyTorch on the internet (the first being the PyTorch documentation).
- Update April 2023: New tutorial for PyTorch 2.0 is live! And because PyTorch 2.0 is an additive (new features) and backward-compatible release, all previous course materials will still work with PyTorch 2.0.
Contents of this page
- Course materials/outline
- About this course
- Status (the progress of the course creation)
- Log (a log of the course material creation process)
Course materials/outline
- 📖 Online book version: All of course materials are available in a readable online book at learnpytorch.io.
- 🎥 First five sections on YouTube: Learn PyTorch in a day by watching the first 25 hours of material.
- 🔬 Course focus: code, code, code, experiment, experiment, experiment.
- 🏃♂️ Teaching style: https://sive.rs/kimo.
- 🤔 Ask a question: See the GitHub Discussions page for existing questions/ask your own.
| Section | What does it cover? | Exercises & Extra-curriculum | Slides |
|---|---|---|---|
| 00 - PyTorch Fundamentals | Many fundamental PyTorch operations used for deep learning and neural networks. | Go to exercises & extra-curriculum | Go to slides |
| 01 - PyTorch Workflow | Provides an outline for approaching deep learning problems and building neural networks with PyTorch. | Go to exercises & extra-curriculum | Go to slides |
| 02 - PyTorch Neural Network Classification | Uses the PyTorch workflow from 01 to go through a neural network classification problem. | Go to exercises & extra-curriculum | Go to slides |
| 03 - PyTorch Computer Vision | Let's see how PyTorch can be used for computer vision problems using the same workflow from 01 & 02. | Go to exercises & extra-curriculum | Go to slides |
| 04 - PyTorch Custom Datasets | How do you load a custom dataset into PyTorch? Also we'll be laying the foundations in this notebook for our modular code (covered in 05). | Go to exercises & extra-curriculum | Go to slides |
| 05 - PyTorch Going Modular | PyTorch is designed to be modular, let's turn what we've created into a series of Python scripts (this is how you'll often find PyTorch code in the wild). | Go to exercises & extra-curriculum | Go to slides |
| 06 - PyTorch Transfer Learning | Let's take a well performing pre-trained model and adjust it to one of our own problems. | Go to exercises & extra-curriculum | Go to slides |
| 07 - Milestone Project 1: PyTorch Experiment Tracking | We've built a bunch of models... wouldn't it be good to track how they're all going? | Go to exercises & extra-curriculum | Go to slides |
| 08 - Milestone Project 2: PyTorch Paper Replicating | PyTorch is the most popular deep learning framework for machine learning research, let's see why by replicating a machine learning paper. | Go to exercises & extra-curriculum | Go to slides |
| 09 - Milestone Project 3: Model Deployment | So we've built a working PyTorch model... how do we get it in the hands of others? Hint: deploy it to the internet. | Go to exercises & extra-curriculum | Go to slides |
| PyTorch Extra Resources | This course covers a large amount of PyTorch and deep learning but the field of machine learning is vast, inside here you'll find recommended books and resources for: PyTorch and deep learning, ML engineering, NLP (natural language processing), time series data, where to find datasets and more. | - | - |
| PyTorch Cheatsheet | A very quick overview of some of the main features of PyTorch plus links to various resources where more can be found in the course and in the PyTorch documentation. | - | - |
| A Quick PyTorch 2.0 Tutorial | A fasssssst introduction to PyTorch 2.0, what's new and how to get started along with resources to learn more. | - | - |
Status
All materials completed and videos published on Zero to Mastery!
See the project page for work-in-progress board - https://github.com/users/mrdbourke/projects/1
- Total video count: 321
- Done skeleton code for: 00, 01, 02, 03, 04, 05, 06, 07, 08, 09
- Done annotations (text) for: 00, 01, 02, 03, 04, 05, 06, 07, 08, 09
- Done images for: 00, 01, 02, 03, 04, 05, 06, 07, 08, 09
- Done keynotes for: 00, 01, 02, 03, 04, 05, 06, 07, 08, 09
- Done exercises and solutions for: 00, 01, 02, 03, 04, 05, 06, 07, 08, 09
See the log for almost daily updates.
About this course
Who is this course for?
You: Are a beginner in the field of machine learning or deep learning and would like to learn PyTorch.
This course: Teaches you PyTorch and many machine learning concepts in a hands-on, code-first way.
If you already have 1-year+ experience in machine learning, this course may help but it is specifically designed to be beginner-friendly.
What are the prerequisites?
- 3-6 months coding Python.
- At least one beginner machine learning course (however this might be able to be skipped, resources are linked for many different topics).
- Experience using Jupyter Notebooks or Google Colab (though you can pick this up as we go along).
- A willingness to learn (most important).
For 1 & 2, I'd recommend the Zero to Mastery Data Science and Machine Learning Bootcamp, it'll teach you the fundamentals of machine learning and Python (I'm biased though, I also teach that course).
How is the course taught?
All of the course materials are available for free in an online book at learnpytorch.io. If you like to read, I'd recommend going through the resources there.
If you prefer to learn via video, the course is also taught in apprenticeship-style format, meaning I write PyTorch code, you write PyTorch code.
There's a reason the course motto's include if in doubt, run the code and experiment, experiment, experiment!.
My whole goal is to help you to do one thing: learn machine learning by writing PyTorch code.
The code is all written via Google Colab Notebooks (you could also use Jupyter Notebooks), an incredible free resource to experiment with machine learning.
What will I get if I finish the course?
There's certificates and all that jazz if you go through the videos.
But certificates are meh.
You can consider this course a machine learning momentum builder.
By the end, you'll have written hundreds of lines of PyTorch code.
And will have been exposed to many of the most important concepts in machine learning.
So when you go to build your own machine learning projects or inspect a public machine learning project made with PyTorch, it'll feel familiar and if it doesn't, at least you'll know where to look.
What will I build in the course?
We start with the barebone fundamentals of PyTorch and machine learning, so even if you're new to machine learning you'll be caught up to speed.
Then we’ll explore more advanced areas including PyTorch neural network classification, PyTorch workflows, computer vision, custom datasets, experiment tracking, model deployment, and my personal favourite: transfer learning, a powerful technique for taking what one machine learning model has learned on another problem and applying it to your own!
Along the way, you’ll build three milestone projects surrounding an overarching project called FoodVision, a neural network computer vision model to classify images of food.
These milestone projects will help you practice using PyTorch to cover important machine learning concepts and create a portfolio you can show employers and say "here's what I've done".
How do I get started?
You can read the materials on any device but this course is best viewed and coded along within a desktop browser.
The course uses a free tool called Google Colab. If you've got no experience with it, I'd go through the free Introduction to Google Colab tutorial and then come back here.
To start:
- Click on one of the notebook or section links above like "00. PyTorch Fundamentals".
- Click the "Open in Colab" button up the top.
- Press SHIFT+Enter a few times and see what happens.
My question isn't answered
Please leave a discussion or send me an email directly: daniel (at) mrdbourke (dot) com.
Log
Almost daily updates of what's happening.
- 15 May 2023 - PyTorch 2.0 tutorial finished + videos added to ZTM/Udemy, see code: https://www.learnpytorch.io/pytorch_2_intro/
- 13 Apr 2023 - update PyTorch 2.0 notebook
- 30 Mar 2023 - update PyTorch 2.0 notebook with more info/clean code
- 23 Mar 2023 - upgrade PyTorch 2.0 tutorial with annotations and images
- 13 Mar 2023 - add starter code for PyTorch 2.0 tutorial
- 18 Nov 2022 - add a reference for 3 most common errors in PyTorch + links to course sections for more: https://www.learnpytorch.io/pytorch_most_common_errors/
- 9 Nov 2022 - add PyTorch cheatsheet for a very quick overview of the main features of PyTorch + links to course sections: https://www.learnpytorch.io/pytorch_cheatsheet/
- 9 Nov 2022 - full course materials (300+ videos) are now live on Udemy! You can sign up here: https://www.udemy.com/course/pytorch-for-deep-learning/?couponCode=ZTMGOODIES7 (launch deal code valid for 3-4 days from this line)
- 4 Nov 2022 - add a notebook for PyTorch Cheatsheet in
extras/(a simple overview of many of the most important functionality of PyTorch) - 2 Oct 2022 - all videos for section 08 and 09 published (100+ videos for the last two sections)!
- 30 Aug 2022 - recorded 15 videos for 09, total videos: 321, finished section 09 videos!!!! ... even bigger than 08!!
- 29 Aug 2022 - recorded 16 videos for 09, total videos: 306
- 28 Aug 2022 - recorded 11 videos for 09, total videos: 290
- 27 Aug 2022 - recorded 16 videos for 09, total videos: 279
- 26 Aug 2022 - add finishing touchs to notebook 09, add slides for 09, create solutions and exercises for 09
- 25 Aug 2022 - add annotations and cleanup 09, remove TK's, cleanup images, make slides for 09
- 24 Aug 2022 - add annotations to 09, main takeaways, exercises and extra-curriculum done
- 23 Aug 2022 - add annotations to 09, add plenty of images/slides
- 22 Aug 2022 - add annotations to 09, start working on slides/images
- 20 Aug 2022 - add annotations to 09
- 19 Aug 2022 - add annotations to 09, check out the awesome demos!
- 18 Aug 2022 - add annotations to 09
- 17 Aug 2022 - add annotations to 09
- 16 Aug 2022 - add annotations to 09
- 15 Aug 2022 - add annotations to 09
- 13 Aug 2022 - add annotations to 09
- 12 Aug 2022 - add demo files for notebook 09 to
demos/, start annotating notebook 09 with explainer text - 11 Aug 2022 - finish skeleton code for notebook 09, course finishes deploying 2x models, one for FoodVision Mini & one for (secret)
- 10 Aug 2022 - add section for PyTorch Extra Resources (places to learn more about PyTorch/deep learning): https://www.learnpytorch.io/pytorch_extra_resources/
- 09 Aug 2022 - add more skeleton code to notebook 09
- 08 Aug 2022 - create draft notebook for 09, end goal to deploy FoodVision Mini model and make it publically accessible
- 05 Aug 2022 - recorded 11 videos for 08, total videos: 263, section 08 videos finished!... the biggest section so far
- 04 Aug 2022 - recorded 13 videos for 08, total videos: 252
- 03 Aug 2022 - recorded 3 videos for 08, total videos: 239
- 02 Aug 2022 - recorded 12 videos for 08, total videos: 236
- 30 July 2022 - recorded 11 videos for 08, total videos: 224
- 29 July 2022 - add exercises + solutions for 08, see live walkthrough on YouTube: https://youtu.be/tjpW_BY8y3g
- 28 July 2022 - add slides for 08
- 27 July 2022 - cleanup much of 08, start on slides for 08, exercises and extra-curriculum next
- 26 July 2022 - add annotations and images for 08
- 25 July 2022 - add annotations for 08
- 24 July 2022 - launched first half of course (notebooks 00-04) in a single video (25+ hours!!!) on YouTube: https://youtu.be/Z_ikDlimN6A
- 21 July 2022 - add annotations and images for 08
- 20 July 2022 - add annotations and images for 08, getting so close! this is an epic section
- 19 July 2022 - add annotations and images for 08
- 15 July 2022 - add annotations and images for 08
- 14 July 2022 - add annotations for 08
- 12 July 2022 - add annotations for 08, woo woo this is bigggg section!
- 11 July 2022 - add annotations for 08
- 9 July 2022 - add annotations for 08
- 8 July 2022 - add a bunch of annotations to 08
- 6 July 2022 - course launched on ZTM Academy with videos for sections 00-07! 🚀 - https://dbourke.link/ZTMPyTorch
- 1 July 2022 - add annotations and images for 08
- 30 June 2022 - add annotations for 08
- 28 June 2022 - recorded 11 videos for section 07, total video count 213, all videos for section 07 complete!
- 27 June 2022 - recorded 11 videos for section 07, total video count 202
- 25 June 2022 - recreated 7 videos for section 06 to include updated APIs, total video count 191
- 24 June 2022 - recreated 12 videos for section 06 to include updated APIs
- 23 June 2022 - finish annotations for 07, add exercise template and solutions for 07 + video walkthrough on YouTube: https://youtu.be/cO_r2FYcAjU
- 21 June 2022 - make 08 runnable end-to-end, add images and annotations for 07
- 17 June 2022 - fix up 06, 07 v2 for upcoming torchvision version upgrade, add plenty of annotations to 08
- 13 June 2022 - add notebook 08 first version, starting to replicate the Vision Transformer paper
- 10 June 2022 - add annotations for 07 v2
- 09 June 2022 - create 07 v2 for
torchvisionv0.13 (this will replace 07 v1 whentorchvision=0.13is released) - 08 June 2022 - adapt 06 v2 for
torchvisionv0.13 (this will replace 06 v1 whentorchvision=0.13is released) - 07 June 2022 - create notebook 06 v2 for upcoming
torchvisionv0.13 update (new transfer learning methods) - 04 June 2022 - add annotations for 07
- 03 June 2022 - huuuuuuge amount of annotations added to 07
- 31 May 2022 - add a bunch of annotations for 07, make code runnable end-to-end
- 30 May 2022 - record 4 videos for 06, finished section 06, onto section 07, total videos 186
- 28 May 2022 - record 10 videos for 06, total videos 182
- 24 May 2022 - add solutions and exercises for 06
- 23 May 2022 - finished annotations and images for 06, time to do exercises and solutions
- 22 May 2202 - add plenty of images to 06
- 18 May 2022 - add plenty of annotations to 06
- 17 May 2022 - added a bunch of annotations for section 06
- 16 May 2022 - recorded 10 videos for section 05, finish videos for section 05 ✅
- 12 May 2022 - added exercises and solutions for 05
- 11 May 2022 - clean up part 1 and part 2 notebooks for 05, make slides for 05, start on exercises and solutions for 05
- 10 May 2022 - huuuuge updates to the 05 section, see the website, it looks pretty: https://www.learnpytorch.io/05_pytorch_going_modular/
- 09 May 2022 - add a bunch of materials for 05, cleanup docs
- 08 May 2022 - add a bunch of materials for 05
- 06 May 2022 - continue making materials for 05
- 05 May 2022 - update section 05 with headings/outline
- 28 Apr 2022 - recorded 13 videos for 04, finished videos for 04, now to make materials for 05
- 27 Apr 2022 - recorded 3 videos for 04
- 26 Apr 2022 - recorded 10 videos for 04
- 25 Apr 2022 - recorded 11 videos for 04
- 24 Apr 2022 - prepared slides for 04
- 23 Apr 2022 - recorded 6 videos for 03, finished videos for 03, now to 04
- 22 Apr 2022 - recorded 5 videos for 03
- 21 Apr 2022 - recorded 9 videos for 03
- 20 Apr 2022 - recorded 3 videos for 03
- 19 Apr 2022 - recorded 11 videos for 03
- 18 Apr 2022 - finish exercises/solutions for 04, added live-coding walkthrough of 04 exercises/solutions on YouTube: https://youtu.be/vsFMF9wqWx0
- 16 Apr 2022 - finish exercises/solutions for 03, added live-coding walkthrough of 03 exercises/solutions on YouTube: https://youtu.be/_PibmqpEyhA
- 14 Apr 2022 - add final images/annotations for 04, begin on exercises/solutions for 03 & 04
- 13 Apr 2022 - add more images/annotations for 04
- 3 Apr 2022 - add more annotations for 04
- 2 Apr 2022 - add more annotations for 04
- 1 Apr 2022 - add more annotations for 04
- 31 Mar 2022 - add more annotations for 04
- 29 Mar 2022 - add more annotations for 04
- 27 Mar 2022 - starting to add annotations for 04
- 26 Mar 2022 - making dataset for 04
- 25 Mar 2022 - make slides for 03
- 24 Mar 2022 - fix error for 03 not working in docs (finally)
- 23 Mar 2022 - add more images for 03
- 22 Mar 2022 - add images for 03
- 20 Mar 2022 - add more annotations for 03
- 18 Mar 2022 - add more annotations for 03
- 17 Mar 2022 - add more annotations for 03
- 16 Mar 2022 - add more annotations for 03
- 15 Mar 2022 - add more annotations for 03
- 14 Mar 2022 - start adding annotations for notebook 03, see the work in progress here: https://www.learnpytorch.io/03_pytorch_computer_vision/
- 12 Mar 2022 - recorded 12 videos for 02, finished section 02, now onto making materials for 03, 04, 05
- 11 Mar 2022 - recorded 9 videos for 02
- 10 Mar 2022 - recorded 10 videos for 02
- 9 Mar 2022 - cleaning up slides/code for 02, getting ready for recording
- 8 Mar 2022 - recorded 9 videos for section 01, finished section 01, now onto 02
- 7 Mar 2022 - recorded 4 videos for section 01
- 6 Mar 2022 - recorded 4 videos for section 01
- 4 Mar 2022 - recorded 10 videos for section 01
- 20 Feb 2022 - recorded 8 videos for section 00, finished section, now onto 01
- 18 Feb 2022 - recorded 13 videos for section 00
- 17 Feb 2022 - recorded 11 videos for section 00
- 16 Feb 2022 - added setup guide
- 12 Feb 2022 - tidy up README with table of course materials, finish images and slides for 01
- 10 Feb 2022 - finished slides and images for 00, notebook is ready for publishing: https://www.learnpytorch.io/00_pytorch_fundamentals/
- 01-07 Feb 2022 - add annotations for 02, finished, still need images, going to work on exercises/solutions today
- 31 Jan 2022 - start adding annotations for 02
- 28 Jan 2022 - add exercies and solutions for 01
- 26 Jan 2022 - lots more annotations to 01, should be finished tomorrow, will do exercises + solutions then too
- 24 Jan 2022 - add a bunch of annotations to 01
- 21 Jan 2022 - start adding annotations for 01
- 20 Jan 2022 - finish annotations for 00 (still need to add images), add exercises and solutions for 00
- 19 Jan 2022 - add more annotations for 00
- 18 Jan 2022 - add more annotations for 00
- 17 Jan 2022 - back from holidays, adding more annotations to 00
- 10 Dec 2021 - start adding annotations for 00
- 9 Dec 2021 - Created a website for the course (learnpytorch.io) you'll see updates posted there as development continues
- 8 Dec 2021 - Clean up notebook 07, starting to go back through code and add annotations
- 26 Nov 2021 - Finish skeleton code for 07, added four different experiments, need to clean up and make more straightforward
- 25 Nov 2021 - clean code for 06, add skeleton code for 07 (experiment tracking)
- 24 Nov 2021 - Update 04, 05, 06 notebooks for easier digestion and learning, each section should cover a max of 3 big ideas, 05 is now dedicated to turning notebook code into modular code
- 22 Nov 2021 - Update 04 train and test functions to make more straightforward
- 19 Nov 2021 - Added 05 (transfer learning) notebook, update custom data loading code in 04
- 18 Nov 2021 - Updated vision code for 03 and added custom dataset loading code in 04
- 12 Nov 2021 - Added a bunch of skeleton code to notebook 04 for custom dataset loading, next is modelling with custom data
- 10 Nov 2021 - researching best practice for custom datasets for 04
- 9 Nov 2021 - Update 03 skeleton code to finish off building CNN model, onto 04 for loading custom datasets
- 4 Nov 2021 - Add GPU code to 03 + train/test loops +
helper_functions.py - 3 Nov 2021 - Add basic start for 03, going to finish by end of week
- 29 Oct 2021 - Tidied up skeleton code for 02, still a few more things to clean/tidy, created 03
- 28 Oct 2021 - Finished skeleton code for 02, going to clean/tidy tomorrow, 03 next week
- 27 Oct 2021 - add a bunch of code for 02, going to finish tomorrow/by end of week
- 26 Oct 2021 - update 00, 01, 02 with outline/code, skeleton code for 00 & 01 done, 02 next
- 23, 24 Oct 2021 - update 00 and 01 notebooks with more outline/code
- 20 Oct 2021 - add v0 outlines for 01 and 02, add rough outline of course to README, this course will focus on less but better
- 19 Oct 2021 - Start repo 🔥, add fundamentals notebook draft v0
Related repositories
Hugging Face Transformers (huggingface/transformers) is a Python library that centralizes model definitions for text, computer vision, audio, video, and multimodal machine learning, covering both inference and training. The README describes it as a pivot point compatible with training frameworks such as Axolotl, DeepSpeed, and PyTorch-Lightning, and inference engines such as vLLM, SGLang, and TGI, with more than 1M+ model checkpoints listed on the Hugging Face Hub.
LLMs-from-scratch is Sebastian Raschka's GitHub companion to the Manning book Build a Large Language Model (From Scratch), ISBN 9781633437166. It walks through building, pretraining, and finetuning a GPT-style model in PyTorch across seven chapters and five appendices, plus a growing set of bonus notebooks covering newer open architectures.
TensorFlow is Google's open-source, end-to-end platform for machine learning, hosted at tensorflow/tensorflow under the Apache-2.0 license. It was originally built within Google Brain's Machine Intelligence team for ML and neural network research, and today it ships stable Python and C++ APIs alongside GPU, CPU-only, and Docker install paths. The README positions it as covering both research work and shipping ML-powered applications.
PyTorch is an open-source Python package hosted at pytorch/pytorch on GitHub, built around two core capabilities: NumPy-like Tensor computation with GPU acceleration, and deep neural networks built through a tape-based autograd system. Its components include torch.nn for network layers, torch.jit for compiling models into TorchScript, and torch.multiprocessing for sharing Tensors across processes, with build support for CUDA, AMD ROCm, and Intel GPU backends documented in the README.
Quick answers
How active is development on mrdbourke/pytorch-deep-learning?
The most recent commit recorded on mrdbourke/pytorch-deep-learning was 6 months ago, based on the GitHub push timestamp. The repository has 5.1k forks — one of the better signals of community interest.
How many stars does mrdbourke/pytorch-deep-learning have?
mrdbourke/pytorch-deep-learning has 18.6k GitHub stars — refresh the page for the live number, or check github.com/mrdbourke/pytorch-deep-learning. TopGit mirrors GitHub's count but does not claim minute-by-minute accuracy.
What else is in the AI Tools space?
mrdbourke/pytorch-deep-learning is tracked by TopGit under the AI Tools category, alongside 3 GitHub-tagged topics. Trending and Topics pages list peer repositories of comparable stars and language.
What language is mrdbourke/pytorch-deep-learning written in?
mrdbourke/pytorch-deep-learning is written primarily in Jupyter Notebook. GitHub's language field is based on the largest share of bytes in the default branch.
What topics is mrdbourke/pytorch-deep-learning associated with?
GitHub's repository topics for mrdbourke/pytorch-deep-learning: "deep-learning", "machine-learning", "pytorch". TopGit's editorial category is AI Tools.
Where do I read more about mrdbourke/pytorch-deep-learning?
This TopGit page is a snapshot — the READ ME tab shows the project's own README content (links stripped, images preserved). The GitHub repository at github.com/mrdbourke/pytorch-deep-learning is the definitive source.
Why is mrdbourke/pytorch-deep-learning categorized under AI Tools?
TopGit places mrdbourke/pytorch-deep-learning in the AI Tools category based on its GitHub topics and description (tagged: "deep-learning", "machine-learning", "pytorch"). Categories are assigned from real repository metadata, not editorial guesswork.
Read full README in the tab above.
Curious whether pytorch-deep-learning is right for you?
Let ChatGPT, Claude, or Perplexity look into it — click below and see what AI actually says about pytorch-deep-learning.