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With this video I'm kicking off a series of tutorials on JAX!

JAX is a powerful and increasingly more popular ML library built by the Google Research team. The 2 most popular deep learning frameworks built on top of JAX are Haiku (DeepMInd) and Flax (Google Research).

In this video I cover the basics as well as the nitty-gritty details of jit, grad, vmap, and various other idiosyncrasies of JAX.

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✅ JAX GitHub: https://github.com/google/jax
✅ JAX docs: https://jax.readthedocs.io/

✅ My notebook: https://github.com/gordicaleksa/get-s...
✅ Useful video on autodiff:    • What is Automatic Differentiation?  
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⌚️ Timetable:
00:00:00 What is JAX? JAX ecosystem
00:03:35 JAX basics
00:10:05 JAX is accelerator agnostic
00:15:00 jit explained
00:17:45 grad explained
00:27:25 The power of JAX autodiff (Hessians and beyond)
00:31:00 vmap explained
00:36:50 JAX API (NumPy, lax, XLA)
00:39:40 The nitty-gritty details of jit
00:46:55 Static arguments
00:50:05 Gotcha 1: Pure functions
00:56:00 Gotcha 2: In-Place Updates
00:57:35 Gotcha 3: Out-of-Bounds Indexing
00:59:55 Gotcha 4: Non-Array Inputs
01:01:50 Gotcha 5: Random Numbers
01:09:40 Gotcha 6: Control Flow
01:13:45 Gotcha 7: NaNs and float32
02:15:25 Quick summary
02:16:00 Conclusion: who should be using JAX?
02:17:10 Outro

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Huge thank you to these AI Epiphany patreons:
Eli Mahler
Petar Veličković
Bartłomiej Danek
Zvonimir Sabljic

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#jax #machinelearning #framework