Learning Modern AI from First Principles: A Comprehensive Project-Driven Study Guide

Original Author/Maintainer: 25621 Source Platform: GitHub Original Link: https://github.com/25621/ai-learning-guides

This open-source project provides a complete, project-driven learning path to help learners understand modern AI technologies from first principles, covering cutting-edge fields from PyTorch fundamentals to multimodal large models, inference optimization, and hardware deployment, and building a complete knowledge system.

Today, with the rapid development of the artificial intelligence field, how to systematically master technical knowledge from basics to cutting-edge has become a challenge for many learners. The ai-learning-guides project created by GitHub user 25621 was born to solve this problem, aiming to provide structured learning resources.

The project covers multiple key areas:

1. Deep learning fundamentals: Focusing on PyTorch, master tensor operations, automatic differentiation, neural network construction, etc.
2. Large language models: Understand Transformer architecture, pre-training/fine-tuning, and alignment techniques.
3. Diffusion models: Learn noise scheduling, sampling strategies, and text-to-image/video generation applications.

Advanced content includes:

• Reinforcement learning: Policy gradients, Q-learning, and LLM alignment applications.
• Robotics: Core issues such as perception, planning, and control.
• Multimodal learning: Fusing information from text, images, audio, etc.
• Engineering practice: Optimization techniques like model quantization, pruning, and distillation, as well as edge/specialized hardware deployment.

The project adopts the "learning by doing" concept, with runnable project code for each field; tutorials are long-form and in-depth, covering background motivation, core principles, and implementation details; knowledge is connected to form a network, building lasting understanding rather than temporary memory.

Target audience: Computer science students (class supplement), developers (transitioning to AI), self-learners (follow the path step by step). Learning recommendations: Master PyTorch fundamentals first, complete each module's project before advancing; follow warehouse updates to get the latest content.

This project provides a structured, practice-oriented AI learning path, not only imparting technical knowledge but also cultivating the ability to explore independently and solve problems. It is a high-quality resource for in-depth understanding of modern AI technologies, laying the foundation for participating in AI innovation work.