Show HN: Learn LLMs LeetCode Style

Summary

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 of Contents Question Set Mostly for beginners to get started with PyTorch. Recommended for those who have a basic understanding of PyTorch and want to practice their skills. These problems are designed to challenge your understanding of PyTorch and deep learning concepts. They require you to implement things from scratch or apply advanced techniques. These problems are for advanced users who want to push their PyTorch skills to the limit. They involve complex architectures, custom layers, and advanced techniques. LLM Set An all new set of questions to help you understand and implement Large Language Models from scratch. Each question is designed to take you one step closer to building your own LLM. What's cool? 🚀 Diverse Questions : Covers beginner to advanced PyTorch concepts (e.g., tensors, autograd, CNNs, GANs, and more). : Covers beginner to advanced PyTorch concepts (e.g., tensors, autograd, CNNs, GANs, and more). Guided Learning: Includes incomplete code blocks ( ... and #TODO ) for hands-on practice along with Answers Getting Started 1. Install Dependencies Install pytorch: Install pytorch locally Some problems need other packages. Install as needed. 2. Structure <E/M/H><ID>/ : Easy/Medium/Hard along with the question ID. : Easy/Medium/Hard along with the question ID. <E/M/H><ID>/qname.ipynb : The question file with incomplete code blocks. : The question file with incomplete code blocks. <E/M/H><ID>/qname_SOLN.ipynb : The corresponding solution file. 3. How to Use Navigate...