JaxTon

💯 JAX exercises

Mission 🚀

To provide 100 JAX exercises over different sections structured as a course or tutorials to teach and learn for beginners, intermediates as well as experts.

JAX

The JAX package in Python is a library for high performance and efficient machine learning research.

It is commonly used for various deep learning tasks and runs seamlessly on CPUs, GPUs as well as TPUs.

Exercises 📖

There are a total of 100 JAX exercises divided into 10 sets of Jupyter Notebooks with 10 exercises each. It is recommended to go through the exercises in order but you may start with any set depending on your expertise.

✅ Structured as exercises & tutorials - Choose your style
✅ Suitable for beginners, intermediates & experts - Choose your level
✅ Available on Colab, Kaggle, Binder & GitHub - Choose your platform
✅ Supports running on CPU, GPU & TPU - Choose your backend

Set 01 • JAX Introduction • Beginner • Exercises 1-10

Style	Colab	Kaggle	Binder	GitHub
Exercises	1st February, 2022	1st February, 2022	1st February, 2022	1st February, 2022
Solutions	1st February, 2022	1st February, 2022	1st February, 2022	1st February, 2022

Set 02 • Data Operations • Beginner • Exercises 11-20

Style	Colab	Kaggle	Binder	GitHub
Exercises	4th February, 2022	4th February, 2022	4th February, 2022	4th February, 2022
Solutions	4th February, 2022	4th February, 2022	4th February, 2022	4th February, 2022

Set 03 • Pseudorandom Numbers • Beginner • Exercises 21-30

Style	Colab	Kaggle	Binder	GitHub
Exercises	7th February, 2022	7th February, 2022	7th February, 2022	7th February, 2022
Solutions	7th February, 2022	7th February, 2022	7th February, 2022	7th February, 2022

Set 04 • Just-In-Time (JIT) Compilation • Beginner • Exercises 31-40

Style	Colab	Kaggle	Binder	GitHub
Exercises	10th February, 2022	10th February, 2022	10th February, 2022	10th February, 2022
Solutions	10th February, 2022	10th February, 2022	10th February, 2022	10th February, 2022

Set 05 • Control Flows • Beginner • Exercises 41-50

Style	Colab	Kaggle	Binder	GitHub
Exercises	13th February, 2022	13th February, 2022	13th February, 2022	13th February, 2022
Solutions	13th February, 2022	13th February, 2022	13th February, 2022	13th February, 2022

Set 06 • Automatic Differentiation • Intermediate • Exercises 51-60

Style	Colab	Kaggle	Binder	GitHub
Exercises	16th February, 2022	16th February, 2022	16th February, 2022	16th February, 2022
Solutions	16th February, 2022	16th February, 2022	16th February, 2022	16th February, 2022

Set 07 • Automatic Vectorization • Intermediate • Exercises 61-70

Style	Colab	Kaggle	Binder	GitHub
Exercises	19th February, 2022	19th February, 2022	19th February, 2022	19th February, 2022
Solutions	19th February, 2022	19th February, 2022	19th February, 2022	19th February, 2022

Set 08 • Pytrees • Intermediate • Exercises 71-80

Style	Colab	Kaggle	Binder	GitHub
Exercises	22nd February, 2022	22nd February, 2022	22nd February, 2022	22nd February, 2022
Solutions	22nd February, 2022	22nd February, 2022	22nd February, 2022	22nd February, 2022

Set 09 • Neural Networks • Expert • Exercises 81-90

Style	Colab	Kaggle	Binder	GitHub
Exercises	25th February, 2022	25th February, 2022	25th February, 2022	25th February, 2022
Solutions	25th February, 2022	25th February, 2022	25th February, 2022	25th February, 2022

Set 10 • Capstone Project • Expert • Exercises 91-100

Style	Colab	Kaggle	Binder	GitHub
Exercises	28th February, 2022	28th February, 2022	28th February, 2022	28th February, 2022
Solutions	28th February, 2022	28th February, 2022	28th February, 2022	28th February, 2022

The Jupyter Notebooks can also be run locally by cloning the repo and running on your local jupyter server.

git clone https://github.com/vopani/jaxton.git
python3 -m pip install notebook
jupyter notebook

P.S. The notebooks will be periodically updated to improve the exercises and support the latest version.

Contribution 🛠️

Please create an Issue for any improvements, suggestions or errors in the content.

chaoshengt/jaxton