This repository provides code and slides for a course on building LLM-powered applications from scratch, targeting machine learning engineers, data scientists, AI researchers, and software engineers. It aims to equip learners with the skills to design, build, and deploy custom LLM solutions by focusing on foundational concepts rather than pre-built frameworks.

How It Works

The course delves into the core components of LLM applications, including Transformer architectures, semantic search, and Retrieval-Augmented Generation (RAG). It emphasizes building these systems from the ground up, covering data preprocessing, model training and fine-tuning, evaluation, and deployment via APIs and Hugging Face. This approach allows for greater customization and optimization compared to relying solely on high-level frameworks.

Quick Start & Requirements

• Installation: Not explicitly detailed, but likely involves standard Python package management.
• Prerequisites: Python programming skills, basic machine learning knowledge.
• Resources: The repository contains 29 lessons and 6 real-world projects.

Highlighted Details

• Focuses on building LLM applications from scratch, avoiding frameworks like LangChain or LlamaIndex.
• Covers Transformer architecture, RAG, semantic search, and LLM deployment.
• Includes practical, real-world projects for hands-on learning.
• Offers insights into training, fine-tuning, and deploying LLMs for enterprise use cases.

Maintenance & Community

The course has been taught to over 1500 professionals at institutions like Stanford and UCLA. Attribution and credit are requested for course material usage.

Licensing & Compatibility

The repository's license is not specified in the README.

Limitations & Caveats

This course is explicitly stated as not for beginners and requires prior Python and basic ML knowledge. The README does not detail specific hardware requirements for running the projects or fine-tuning models.