I can safely say PyTorch is on that list of deep learning libraries. It has helped accelerate the research that goes into deep learning models by making them computationally faster and less expensive (a data scientist’s dream!).
I’ve personally found PyTorch really useful for my work. I delve heavily into the arts of computer vision and find myself leaning on PyTorch’s flexibility and efficiency quite often.
So in this article, I will guide you on how PyTorch works, and how you can get started with it today itself. We’ll cover everything there is to cover about this game-changing deep learning library and also take up a really cool case study to see PyTorch in action.
1. Getting Started with PyTorch
2. Basics of PyTorch
3. Introduction to Tensors
4. Mathematical Operations
5. Matrix Initialization and Matrix Operations
6. Common PyTorch Modules
7. Autograd
8. Optim
9. nn
10.Building a Neural Network from Scratch in PyTorch
11.Solving an Image Classification Problem using PyTorch
12.What’s Next?