TensorTonic Launches JAX for ML Systems Study Plan With 35 Hands-On Problems
burny_tech · x · 2026-11-10
TensorTonic launched a new study plan, "JAX for ML Systems," built around machine learning systems internals. It includes 35 coding problems (10 easy / 16 medium / 9 hard) across 9 sections, from functional arrays and PRNG key splitting to jit/vmap/pytree optimizers.
Topics include computing per-example gradients with vmap, mixed-precision training with loss scaling, building a paged KV cache, speculative decoding, and parallel training. The pitch: PyTorch taught you to train models; JAX teaches you the systems underneath.
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