Free PyTorch Courses/Books
Introduction
Recently, I’ve been wanting to get into machine learning with PyTorch, and I looked for resources that were simple to understand with minimal math (I am still a high school student so I do not know very advanced math concepts, yet). Below is a list of some of the courses I have found so far, and am either planning to learn or am already using it to learn:
1. PyTorch for Deep Learning & Machine Learning — Full Course
This is a course that I have currently been doing, and it has been amazing! The instructor can teach concepts in such a simple manner, and, while there may be a lot of repetiveness in the course, I have found it to be a really good thing because it helps me gain confidence when I go to write my own machine learning models. The course starts off with talking about the basics of PyTorch (and really all machine learning frameworks), tensors, and different manipulations that can be done on them. Then you will go onto building models such a simple linear regression model, binary classification, and multi-class classification. After this you will be working with computer vision and creating models to deal with images. The overall course is very engaging and fun, and I would highly recommend doing it.
2. Deep Neural Networks with PyTorch
I haven’t been doing this course, but might do it if I really want to get some more practice in. I’ve looked through the modules, and the things do seem quite similar to the first course, but there is not any computer vision. It seems like a good course if you don’t want to do compter vision, and do a bit deeper in some topics, but I wanted to learn computer vision as well so I chose the first course.
3. Deep Learning with PyTorch
If you prefer reading instead of watching videos then this book might just be for you. It goes fairly deep into PyTorch and machine learning concepts, and has quite a bit of detail. This book is definetly quite long and also requires some more advanced math concepts such as vectors, matrices, and dot products (not too complicated though). A great thing about this book is that it goes a bit into how PyTorch works behind the scenes in part 1, and then in part 2 it uses a real life example of detecting lung cancer to teach you more concepts. If you are someone that wants to go really deep into PyTorch and machine learning then this book might be a good choice.
https://isip.piconepress.com/courses/temple/ece_4822/resources/books/Deep-Learning-with-PyTorch.pdf
Conclusion
I hope that you found some resources that you like from this page, and have fun learning!
