Your Complete AI Learning Roadmap: From Basics to Large Model Mastery
This guide presents a comprehensive AI learning roadmap, dividing study into five progressive stages—from foundational math and programming to core deep‑learning and reinforcement‑learning techniques, large‑model training, industry applications, and future trends—plus curated book lists, tool recommendations, and practical RAG tutorials.
AI Roadmap
Learning AI is a systematic engineering effort that requires a step‑by‑step mastery of foundational knowledge, core technologies, and cutting‑edge applications. The roadmap is divided into five stages.
Stage 1: AI Basics
Before diving into AI, acquire essential mathematics, statistics, and programming skills to build a solid foundation.
Stage 2: Core AI Technologies
Study the core techniques of deep learning and reinforcement learning, which enable the understanding and construction of complex AI models.
Stage 3: Large Models and Frontier Technologies
Large models are the current hotspot in AI research and deployment. Learn how they are trained, the key technologies behind them, and typical application scenarios.
Stage 4: Industry Applications
Apply AI techniques across various domains to improve efficiency and quality, covering sectors such as recommendation systems, medical information retrieval, talent management, finance, power‑grid maintenance, software development, education, transportation, automotive Q&A, gaming, smart offices, and digital banking assistants.
Stage 5: Future Trends
Stay informed about the latest research directions and industry trends to maintain a leading position in the rapidly evolving AI field.
Learning Outline
The following training outline is derived from the Chinese Academy of Sciences Talent Exchange Development Center’s advanced workshop notice.
Self‑Study Resource List
Recommended books and resources for each stage are listed below.
Stage 1: AI Basics
Linear Algebra and Its Applications – Gilbert Strang
Calculus – James Stewart
Linear Algebra & Calculus – Tongji University Mathematics Department
Probability and Statistics – Morris H. DeGroot
Probability and Statistics (Chinese edition) – Sheng Zuo, Pu Xiaolong, Xie Shiqian
Python Crash Course – Eric Matthes
Stage 2: Core AI Technologies
Deep Learning – Ian Goodfellow, Yoshua Bengio, Aaron Courville
Reinforcement Learning: An Introduction – Richard S. Sutton, Andrew G. Barto
The Elements of Statistical Learning – Trevor Hastie, Robert Tibshirani, Jerome Friedman
Statistical Learning Method – Hang Li
Machine Learning – Zhou Zhihua (the “Watermelon Book”)
GPT Illustrated: How Large Models Are Built – Huang Jia
This Is ChatGPT – Stephen Wolfram
Additional Topics
Resources on large‑model tools (e.g., Copilot, Tongyi Lingma), paper‑reading assistants (ChatPaper), intelligent assistants (Kimi), Retrieval‑Augmented Generation (RAG) frameworks such as LangChain and LlamaIndex, and practical tutorials on model deployment (vLLM) and fine‑tuning are also covered in the training curriculum.
Model Perspective
Insights, knowledge, and enjoyment from a mathematical modeling researcher and educator. Hosted by Haihua Wang, a modeling instructor and author of "Clever Use of Chat for Mathematical Modeling", "Modeling: The Mathematics of Thinking", "Mathematical Modeling Practice: A Hands‑On Guide to Competitions", and co‑author of "Mathematical Modeling: Teaching Design and Cases".
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