This is it. This is the complete PyTorch for machine learning and deep learning that you’ve been looking for. It’s thorough. It’s informative. It’s relevant. And it’s absolutely free.
The course PyTorch for Deep Learning & Machine Learning from freeCodeCamp is put together by machine learning stalwart Daniel Bourke. You might know Daniel from his prolific online presence over the past number of years, where he has blogged and created content related to machine learning for some time now.
Here is an overview of the course, straight from Daniel himself:
This course will teach you the foundations of machine learning and deep learning with PyTorch (a machine learning framework written in Python).
The course is video based. However, the videos are based on the contents of this online book.
The course covers everything you need to know to get up to speed with PyTorch and deep learning. And when I say everything, I kind of mean it: the video course is 25 hours long. That’s right, it’s more than a full day. Literally.
The course is split up into the following chapters.
1. PyTorch Fundamentals
This chapter covers introductory topics, including an intro to PyTorch, deep learning, getting set up for the course, and an intro to the basic building block of deep learning, tensors, and their basic functionality.
2. PyTorch Workflow
This chapter gets into PyTorch, and introduces its workflow. You learn about models, train a model, evaluate a model, and save and load a model. You also write the code to do all of this as you go, learning by doing.