# \#3 JAX Course - Future of ML research in JAX/Flax

**URL:** <https://community.wandb.ai/t/3-jax-course-future-of-ml-research-in-jax-flax/1493>\
**Category:** JAX\
**Created:** [December 9, 2021, 7:59am UTC](https://community.wandb.ai/t/3-jax-course-future-of-ml-research-in-jax-flax/1493 "2021-12-09T07:59:17Z")\
**Posts on this page:** 1\
**Showing post:** 10

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**Author:** ![bhutanisanyam1](https://sea2.discourse-cdn.com/flex020/user_avatar/community.wandb.ai/bhutanisanyam1/32/18_2.png) [@bhutanisanyam1](https://community.wandb.ai/u/bhutanisanyam1)\
**Post date:** [December 11, 2021, 4:40pm UTC](https://community.wandb.ai/t/3-jax-course-future-of-ml-research-in-jax-flax/1493/10 "2021-12-11T16:40:39Z")

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Here are the link from prev sessions @kaczmarj:

> [@\#1 JAX course - Interview with Cristian Garcia, JAX 201 and in the DL ecosystem](https://community.wandb.ai/t/1-jax-course-interview-with-cristian-garcia-jax-201-and-in-the-dl-ecosystem/1389/14):
>
> As promised, link to the colab being discussed today: [Google Colab](https://colab.research.google.com/drive/1DcRruSQkLVgc4vZ-qCJB0b-t2YZQUny0?usp=sharing)

> [@\#2 JAX course - Working with Neural Networks in JAX](https://community.wandb.ai/t/2-jax-course-working-with-neural-networks-in-jax/1455/10):
>
> Here are some of the relevant resources from today’s Lecture:
> 
> - Here is a link to the AutoDiff Notebook from Akash: [TF\_JAX\_Tutorials - Part 9 (Autodiff in JAX) | Kaggle](https://www.kaggle.com/aakashnain/tf-jax-tutorials-part-9-autodiff-in-jax)
> - Here’s the PyTree Notebook from Akash: [TF\_JAX\_tutorials - Part 10 (Pytrees in JAX) | Kaggle](https://www.kaggle.com/aakashnain/tf-jax-tutorials-part-10-pytrees-in-jax)
> - Here’s the link to “Flax Basics” from the Flax docs: [Google Colab](https://colab.research.google.com/github/google/flax/blob/main/docs/notebooks/flax_basics.ipynb)
> - Here’s the link to “Annotated MNIST” from the Flax docs: [Google Colab](https://colab.research.google.com/github/google/flax/blob/main/docs/notebooks/annotated_mnist.ipynb)
> - Here’s a link to the UNET lecture from last month: [PyTorch Book Reading - 8. U-net, Image Segmentation and Image Augmentations in PyTorch - YouTube](https://youtu.be/b_6x9BYjmj4)
> - Here’s the Scenic implementation of U-net: [scenic/unet.py at main · google-research/scenic · GitHub](https://github.com/google-research/scenic/blob/main/scenic/projects/baselines/unet.py)

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