Hello and Welcome!

In July 2021, I decided to learn in depth about Pytorch. Why? - Because it's a beautifully written library and Deep Learning is a fascinating subject!

Anyways, my aim is to add all the stuff I've learnt here, in this repo, in a way programmers can understand. Most of the content is based on the amazing book https://www.manning.com/books/deep-learning-with-pytorch Who should read it? - Mostly, it will be I who will refer to these notes. But if you do stumble across this repo, well and good!

The order to read should be as follows -

1. image_classifier.py - It's a nice one to go through to get an idea of Pytorch's ease of use
2. cycle_gan.py - A few more advanced networks (all are prebuilt and loaded from disk, no getting into training yet!)
3. tensors_1.py - This is where the deep dives start, I cover everything required to jump onto more advanced stuff like named tensors, underlying storage objects, serialization/deserialization etc.