How I learned PyTorch in 4 steps:

1. Tutorial: Learn the Basics

This is where you should start if you know nothing about PyTorch.

You should be comfortable using Python.

The tutorial assumes you have basic notions of linear algebra and calculus, but don't let that stop you. You can always take it slow and learn anything that doesn't make sense.

This tutorial will cover the following:

1. Tensors
2. Datasets and DataLoaders
3. Transforms
4. Build Model
5. Automatic differentiation
6. Optimization loop
7. Save, Load and Use Model

https://t.co/XgKZpOQd1S

2. YouTube: Introduction to PyTorch

There's a similar tutorial for those who prefer video content. Same prerequisites as before.

This YouTube series starts from scratch and covers the following:

1. Introduction to PyTorch
2. Introduction to PyTorch Tensors
3. The Fundamentals of Autograd
4. Building Models with PyTorch
5. PyTorch TensorBoard Support
6. Training with PyTorch
7. Model Understanding with Captum

https://t.co/ZElJfqAJOI

3. Learning PyTorch with Examples

This one will be quick, but it will tie everything you learned up to this point.

You'll learn by looking at different examples. That's the best way I process and retain information.

https://t.co/iAcbr05BwI

4. Start writing code

No amount of reading will help you more than writing code.

This step will look different to everyone, but in my case, here is what I did:

• Started writing PyTorch for my work.

• The Keras website is full of examples. I translated a few of them to PyTorch.

Conclusions

You don't have to go far to learn PyTorch. Most of what you need is on their website. Their tutorials cover a lot of ground.

I hope this helps.

By the way, remember that reading is 10% of the effort. Writing code using what you read is the other 90%.