I'd like to learn PyTorch. So I'm going to use this repo to:

1. Add what I've learned.
2. Teach others in a beginner-friendly way.

Stay tuned to here for updates, course materials are being actively worked on.

Launch early 2022.

• Note: This is rough and subject to change.
• Course focus: code, code, code, experiment, experiment, experiment
• Teaching style: https://sive.rs/kimo

0. PyTorch fundamentals - ML is all about representing data as numbers (tensors) and manipulating those tensors so this module will cover PyTorch tensors.
1. PyTorch workflow - You'll use different techniques for different problem types but the workflow remains much the same:

data -> build model -> fit model to data (training) -> evaluate model and make predictions (inference) -> save & load model

Module 1 will showcase an end-to-end PyTorch workflow that can be leveraged for other problems.

2. PyTorch classification - Let's take the workflow we learned in module 1 and apply it to a common machine learning problem type: classification (deciding whether something is one thing or another).
3. PyTorch computer vision - We'll get even more specific now and see how PyTorch can be used for computer vision problems though still using the same workflow from 1 & 2. We'll also start functionizing the code we've been writing, for example: def train(model, data, optimizer, loss_fn): ...
4. PyTorch custom datasets - How do you load a custom dataset into PyTorch? Also we'll be laying the foundations in this notebook for our modular code (covered in 05).
5. Going modular - PyTorch is designed to be modular, let's turn what we've created into a series of Python scripts (this is how you'll often find PyTorch code in the wild). For example:

code/
    data_setup.py <- sets up data
    model_builder.py <- builds the model ready to be used
    engine.py <- training/eval functions for the model
    train.py <- trains and saves the model

6. PyTorch transfer learning - Let's improve upon the models we've built ourselves using transfer learning.
7. PyTorch experiment tracking - We've built a bunch of models... wouldn't it be good to track how they're all going?
8. PyTorch paper replicating - Let's see why PyTorch is the most popular deep learning framework for machine learning research by replicating a machine learning research paper with it.

Each notebook will teach a maximum of 3 big ideas.

• Working on: annotating code notebook 03
• Total video count: 90
• Next: annotate notebooks 03, 04, 05 with text then make slides and images
• Done skeleton code for: 00, 01, 02, 03, 04, 05, 06, 07
• Done annotations (text) for: 00, 01, 02
• Done images for: 00, 01, 02
• Done keynotes for: 00, 01, 02
• Done vidoes for: 00, 01, 02

See the project page for specifics - https://github.com/users/mrdbourke/projects/1

High-level overview of things to do:

• How to use this repo (e.g. env setup, GPU/no GPU) - all notebooks should run fine in Colab and locally if needed.
• Finish skeleton code for notebooks 00 - 07 ✅
• Write annotations for 00 - 07
• Make images for 00 - 07
• Make slides for 00 - 07
• Record videos for 00 - 07

Almost daily updates of what's happening.

• 17 Mar 2022 - add more annotations for 03
• 16 Mar 2022 - add more annotations for 03
• 15 Mar 2022 - add more annotations for 03
• 14 Mar 2022 - start adding annotations for notebook 03, see the work in progress here: https://www.learnpytorch.io/03_pytorch_computer_vision/
• 12 Mar 2022 - recorded 12 videos for 02, finished section 02, now onto making materials for 03, 04, 05
• 11 Mar 2022 - recorded 9 videos for 02
• 10 Mar 2022 - recorded 10 videos for 02
• 9 Mar 2022 - cleaning up slides/code for 02, getting ready for recording
• 8 Mar 2022 - recorded 9 videos for section 01, finished section 01, now onto 02
• 7 Mar 2022 - recorded 4 videos for section 01
• 6 Mar 2022 - recorded 4 videos for section 01
• 4 Mar 2022 - recorded 10 videos for section 01
• 20 Feb 2022 - recorded 8 videos for section 00, finished section, now onto 01
• 18 Feb 2022 - recorded 13 videos for section 00
• 17 Feb 2022 - recorded 11 videos for section 00
• 16 Feb 2022 - added setup guide
• 12 Feb 2022 - tidy up README with table of course materials, finish images and slides for 01
• 10 Feb 2022 - finished slides and images for 00, notebook is ready for publishing: https://www.learnpytorch.io/00_pytorch_fundamentals/
• 01-07 Feb 2022 - add annotations for 02, finished, still need images, going to work on exercises/solutions today
• 31 Jan 2022 - start adding annotations for 02
• 28 Jan 2022 - add exercies and solutions for 01
• 26 Jan 2022 - lots more annotations to 01, should be finished tomorrow, will do exercises + solutions then too
• 24 Jan 2022 - add a bunch of annotations to 01
• 21 Jan 2022 - start adding annotations for 01
• 20 Jan 2022 - finish annotations for 00 (still need to add images), add exercises and solutions for 00
• 19 Jan 2022 - add more annotations for 00
• 18 Jan 2022 - add more annotations for 00
• 17 Jan 2022 - back from holidays, adding more annotations to 00
• 10 Dec 2021 - start adding annoations for 00
• 9 Dec 2021 - Created a website for the course (learnpytorch.io) you'll see updates posted there as development continues
• 8 Dec 2021 - Clean up notebook 07, starting to go back through code and add annotations
• 26 Nov 2021 - Finish skeleton code for 07, added four different experiments, need to clean up and make more straightforward
• 25 Nov 2021 - clean code for 06, add skeleton code for 07 (experiment tracking)
• 24 Nov 2021 - Update 04, 05, 06 notebooks for easier digestion and learning, each section should cover a max of 3 big ideas, 05 is now dedicated to turning notebook code into modular code
• 22 Nov 2021 - Update 04 train and test functions to make more straightforward
• 19 Nov 2021 - Added 05 (transfer learning) notebook, update custom data loading code in 04
• 18 Nov 2021 - Updated vision code for 03 and added custom dataset loading code in 04
• 12 Nov 2021 - Added a bunch of skeleton code to notebook 04 for custom dataset loading, next is modelling with custom data
• 10 Nov 2021 - researching best practice for custom datasets for 04
• 9 Nov 2021 - Update 03 skeleton code to finish off building CNN model, onto 04 for loading custom datasets
• 4 Nov 2021 - Add GPU code to 03 + train/test loops + helper_functions.py
• 3 Nov 2021 - Add basic start for 03, going to finish by end of week
• 29 Oct 2021 - Tidied up skeleton code for 02, still a few more things to clean/tidy, created 03
• 28 Oct 2021 - Finished skeleton code for 02, going to clean/tidy tomorrow, 03 next week
• 27 Oct 2021 - add a bunch of code for 02, going to finish tomorrow/by end of week
• 26 Oct 2021 - update 00, 01, 02 with outline/code, skeleton code for 00 & 01 done, 02 next
• 23, 24 Oct 2021 - update 00 and 01 notebooks with more outline/code
• 20 Oct 2021 - add v0 outlines for 01 and 02, add rough outline of course to README, this course will focus on less but better
• 19 Oct 2021 - Start repo 🔥, add fundamentals notebook draft v0