pytorch-deep-learning README

The project's README from github.com/mrdbourke/pytorch-deep-learning. As one markdown file: README.md.

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).

pytorch deep learning by zero to mastery cover photo with different sections of the course

Contents of this page

Course materials/outline

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

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?

  1. 3-6 months coding Python.
  2. At least one beginner machine learning course (however this might be able to be skipped, resources are linked for many different topics).
  3. Experience using Jupyter Notebooks or Google Colab (though you can pick this up as we go along).
  4. 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:

  1. Click on one of the notebook or section links above like "00. PyTorch Fundamentals".
  2. Click the "Open in Colab" button up the top.
  3. 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.

pytorch-deep-learning (mrdbourke/pytorch-deep-learning)

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Pytorch-deep-learning
What is this?Free course materials for the Zero to Mastery Learn PyTorch for Deep Learning course — a hands-on, code-first introduction to deep learning. The author jokes it's the second best place to learn PyTorch on the internet, after the official docs.
What exactly do I get — a book, videos, or code?All three, for the same 10 sections: an online book at learnpytorch.io, Jupyter notebooks with exercises, solutions and slides, and video — the first five sections are free on YouTube as one 25-hour video.
How do I start going through it?Open any section notebook from this repo, click the Open in Colab button at the top and run cells with SHIFT+Enter. New to Colab? The README links a free introductory tutorial first.
Do I need machine learning experience to follow it?It's built for ML beginners: about 3-6 months of Python coding, ideally one beginner ML course (skippable), and some Jupyter/Colab familiarity. With 1+ year of ML experience it may help less.
Is the course finished, and will it work with current PyTorch?It's complete: all 10 sections done, with 321 videos, exercises and solutions. Materials were updated for PyTorch 2.0 in April 2023 and stay valid, since that release is backward-compatible.
Do I need to install PyTorch or a GPU on my own machine?No — everything runs in Google Colab in the browser (a desktop browser works best), so there is nothing to install locally. Jupyter Notebooks on your own machine work too.
What will I actually build by the end?You go from fundamentals to three milestone projects around FoodVision, a neural network that classifies images of food — covering classification, custom datasets, transfer learning, experiment tracking, paper replication and model deployment. They're meant as portfolio pieces you can show employers.
How much video material is there, and where?321 videos in total. The first five sections (fundamentals through custom datasets) are free on YouTube as a single 25-hour video; the full course is also published on Zero to Mastery and Udemy.
Do I get a certificate?Certificates exist if you go through the video versions, but the author calls them meh — the real payoff is having written hundreds of lines of PyTorch code and covered the core machine learning concepts.
Is there a quicker option if I don't want the full course?Yes — the extras include a PyTorch Cheatsheet notebook with a quick overview of the main features, a fast A Quick PyTorch 2.0 Tutorial, and a list of further books and resources on deep learning, ML engineering, NLP, time series and datasets.
What are similar projects?pytorch-handbook, fastbook, TensorFlow-Course, fastai, machine-learning-zoomcamp, DeepLearning, CV, ai-edu
Which topics is it in?colab, datasets, youtube, curriculum, dataset, course, neural-network, jupyter notebook, nlp, data-science, beginner-friendly, computer-vision
Creator
mrdbourke
Size
784.9 MB
Stars
19.2K
Updated
CommitSep 27
Age
4y
Commits · 90 Days