LeetLLM

Master the skills needed to design, train, and deploy production-grade Artificial Intelligence and Large Language Model (LLM) systems. LeetLLM is a progressive, curriculum-driven learning platform built specifically for software developers, ML engineers, and computer science students. Unlike passive tutorials, LeetLLM focuses on active, hands-on understanding. Learn how transformer architectures function from the ground up, implement backpropagation from scratch, optimize tensor shapes in PyTorch, and master distributed LLM serving. ### Key Features: - **158 Progressive Lessons:** Walk through a structured, 14-phase roadmap covering python for AI, NumPy, linear algebra, probability, PyTorch training loops, Retrieval-Augmented Generation (RAG), and agentic workflows. - **Interactive Quizzes & Scoring:** Reinforce your understanding with active quiz challenges. The app tracks your attempts and saves your best scores. - **Fully Offline-First:** Study and read lessons, review diagrams, and test yourself anywhere. The entire curriculum and all interactive resources are fully pre-bundled inside the app. - **AI & ML Glossary:** Instant access to a rich directory of 320+ technical terms, formulas, and architectural definitions. - **Progress Syncing:** Log in securely via email to sync your read history and quiz accomplishments across devices, or use the app completely as a Guest. ### Curriculum Covered: 1. **AI Foundations:** NumPy tensor shapes, broadcasting, data structures, and probability. 2. **Machine Learning from Scratch:** Backpropagation mechanics, linear regression, gradient descent, and PyTorch training loops. 3. **Core LLM Architectures:** Transformer self-attention, tokenizers, decoding algorithms, and beam search. 4. **Production System Design:** Feature pipelines, ranking, forecasting, model monitoring, and A/B testing. 5. **Advanced Lab Builds:** Shipping document QA products, fine-tuning classifiers, deploying evaluation dashboards, and building production LLM agents. Take control of your AI engineering path. Build implementation speed, master the underlying math, and gain the credentials to deploy production-grade machine learning systems.