video1.0<iframe src="https://www.loom.com/embed/ea3fc8d5e8b64c6597911eb75d9d0baf" frameborder="0" width="1680" height="1260" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12601680Loomhttps://www.loom.com12601680https://cdn.loom.com/sessions/thumbnails/ea3fc8d5e8b64c6597911eb75d9d0baf-297ab15fdcdc29ad.gif258.079In this video, I introduce JAX, Google's high-performance machine learning framework, emphasizing its unique features like automatic differentiation, JIT compilation, and functional programming principles. We explore JAX's array operations, transformations like JIT, Grad, and Vmap, and how they enhance performance in neural network training. I also demonstrate building neural networks from scratch using pure functions and the Flax library for a clean, modular design. Finally, I outline the training pipeline with Optax for efficient updates. I encourage you to dive into these concepts and experiment with JAX in your projects.