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Show HN: Learn LLMs LeetCode Style

TorchLeet is broken into two sets of questions: Question Set: A collection of PyTorch practice problems, ranging from basic to hard, designed to enhance your skills in deep learning and PyTorch. LLM Set: A new set of questions focused on understanding and implementing Large Language Models (LLMs) from scratch, including attention mechanisms, embeddings, and more. Note Avoid using GPT. Try to solve these problems on your own. The goal is to learn and understand PyTorch concepts deeply. Table o

Showh HN: Microjax – JAX in two classes and six functions

Microjax: JAX in two classes and six functions or Read on Github (I recommend actually running the notebook, either on your own computer or Colab). This is inspired by Andrej Karpathy's Micrograd, a PyTorch-like library in about 150 lines of code. Despite PyTorch's popularity, I prefer the way JAX works because it a more functional style. This tutorial borrows heavily from Matthew J Johnson's great 2017 presentation on the predecessor to JAX, autograd: Video / Slides / Code. My main contribut

Showh HN: Microjax - Jax in two classes and six functions

Microjax: JAX in two classes and six functions or Read on Github (I recommend actually running the notebook, either on your own computer or Colab). This is inspired by Andrej Karpathy's Micrograd, a PyTorch-like library in about 150 lines of code. Despite PyTorch's popularity, I prefer the way JAX works because it a more functional style. This tutorial borrows heavily from Matthew J Johnson's great 2017 presentation on the predecessor to JAX, autograd: Video / Slides / Code. My main contribut