JAX Machine Learning Practice: From Zero to Implementing Basic to Modern Models

This article introduces a practical project using the JAX framework to implement machine learning models from scratch, covering a complete learning path from basic algorithms to modern deep learning architectures. JAX has become a new利器 (powerful tool) in research with features like functional programming, automatic differentiation, and GPU/TPU acceleration. This project helps learners deeply understand algorithm principles and engineering practices through the 'implement from scratch' approach.

JAX is a high-performance ML framework developed by Google, extended from NumPy but with different computation patterns. Three core features: automatic differentiation (supports forward/backward and higher-order derivatives), just-in-time compilation (XLA optimizes GPU/TPU performance), vectorized mapping (vmap for automatic batch computation), and it also supports multi-device parallelism (pmap). These features give it unique advantages in research.

The project adopts the 'implement from scratch' approach, requiring hands-on writing of core algorithm components. This method deepens understanding of mathematical principles (e.g., gradient descent for linear regression, backpropagation for neural networks), cultivates engineering skills (numerical stability, memory efficiency), and builds learning confidence.

Models cover from basic to modern: basic ML (linear/logistic regression, SVM, decision tree/random forest, K-nearest neighbors/K-means); deep learning (MLP, CNN, RNN/LSTM/GRU, autoencoder); modern architectures (attention mechanism, Transformer, GAN, VAE), building a knowledge system step by step.

For NumPy users, JAX is compatible but has differences: 1. Arrays are immutable (returns new arrays); 2. Requires pure functions (no side effects); 3. Random number generation needs explicit state passing. Understanding these is key to writing correct JAX code.

JAX uses JIT compilation (@jit decorator) to leverage XLA for optimized execution; vmap simplifies code and improves parallelism via automatic batching; pmap supports distributed training on multiple GPUs/TPUs. These features enable significant acceleration of JAX code on hardware.

JAX is widely used in scientific research: scientific computing (physical simulation, molecular dynamics), deep learning research (ViT, AlphaFold), probabilistic programming (Bayesian inference combined with NumPyro/BlackJAX), favored by researchers for its flexibility and performance.

Learning recommendations: Start with NumPy basics → Understand JAX's functional paradigm → Practice from simple models (linear regression). Recommended resources: official documentation, GitHub examples, community discussions. Summary: JAX balances Python ease of use with native performance; the 'implement from scratch' project helps in in-depth learning, and it is expected to become one of the mainstream frameworks in the future.