Welcome to the PyTorch Fundamentals Learning Path! 🎉
This course is designed to take someone with minimal machine learning experience and get them started on a path toward becoming proficient with the PyTorch open source framework.
This course is going to be broken up into 4 lessons:
- Basics about PyTorch's Tensors - the core data structure in PyTorch
- PyTorch core concepts and workflow
- Classification Neural Networks concepts with PyTorch
- PyTorch tools to process your own data
The course will also have two assignments, one after the first two lessons and one after the latter two. These assignments will be designed to test your knowledge by using the things you've learned in practice and make sure you're on the right track. The assignments can be found in the assignments folder.
To get the most out of this course, you should have some basic knowledge about Python (and, preferably, Jupyter Notebook) and some mathematics fundamentals, like basic calculus (linear, logarithmic and exponential functions, derivatives), linear algebra (vectors, matrices, matrix multiplication) and some statistics knowledge. That said, even if you're not totally comfortable with these topics, you can easily pick things up as you go along.
Having some machine learning essentials in your arsenal will also helpful as it'll make taking the course quicker for you, but they're not required. You'll be able to learn them throughout the course, as it is designed to learn machine learning by using and applying PyTorch.
The course is designed to be taken in a linear fashion, as each lesson builds on top of the previous one. Thus, it's recommended that you take the lessons in order, but you can also skip around if you want to.
The lessons are structured to point you to great free, open source resources that you can use to learn about this topic. Why reinvent the wheel, right? However, you're more than welcome to explore other resources like videos, blog posts or the PyTorch Documentation itself, and if you find things that you think would be helpful to others or fit well in this course, feel free to share them with us!
We want to focus on learning-by-doing, so you're encouraged to do the exercises recommended in the lessons and try things out for yourself as you go along. This will help you solidify your knowledge and make sure you're on the right track. There will also be Extra sections that will point you to some additional resources that you can use to learn more about the topic.
To start this course, make sure you follow the instructions in the Setup Notebook to get your environment ready. We recommend having a Virtual Environment for this course, so you can keep your dependencies separate from your other projects, but it's up to you really.
After you're done with the setup, you can start with the first lesson, Lesson 1: PyTorch Tensors. Have fun! 🚀