AI-LLM-ML-CS-Quant-Overview
Overview of AI/LLM, Machine Learning, Computer Science, and Quantitative Finance industry trends.
Stars: 52
AI-LLM-ML-CS-Quant-Overview is a repository providing overview notes on AI, Large Language Models (LLM), Machine Learning (ML), Computer Science (CS), and Quantitative Finance. It covers various topics such as LangGraph & Cursor AI, DeepSeek, MoE (Mixture of Experts), NVIDIA GTC, LLM Essentials, System Design, Computer Systems, Big Data and AI in Finance, Econometrics and Statistics Conference, C++ Design Patterns and Derivatives Pricing, High-Frequency Finance, Machine Learning for Algorithmic Trading, Stochastic Volatility Modeling, Quant Job Interview Questions, Distributed Systems, Language Models, Designing Machine Learning Systems, Designing Data-Intensive Applications (DDIA), Distributed Machine Learning, and The Elements of Quantitative Investing.
README:
Overview notes on AI, LLM, Machine Learning, Computer Science & Quant Finance.
- 1. NVIDIA GTC | AI Conference for Developers
- 2. DeepSeek
- 3. LangGraph & Cursor AI
- 4. LLM Essentials
- 5. LLM Foundations
- 6. System Design
- 7. Computer Systems
- 8. Big Data and AI in Finance, Econometrics and Statistics Conference, UChicago 2024
- 9. C++ Design Patterns and Derivatives Pricing
- 10. High-Frequency Finance
- 11. Machine Learning for Algorithmic Trading
- 12. Stochastic Volatility Modeling
- 13. Quant Job Interview Questions
Educative: Everything You Need to Know About DeepSeek | Notes
- Ed Donner: LLM Engineering: Master AI, Large Language Models & Agents
- Eden Marco: LangChain-Develop LLM powered applications with LangChain
- Eden Marco: LangGraph-Develop LLM powered AI agents with LangGraph
- Eden Marco: Cursor Course: FullStack development with Cursor AI Copilot
GitHub Projects
- Code-Interpreter-ReAct-LangChain-Agent
- LLM-Documentation-Chatbot
- Cognito-LangGraph-RAG
- LangGraph-Reflection-Researcher
- Cursor-FullStack-AI-App
Educative: Advanced RAG Techniques - Choosing the Right Approach | Notes
Educative: Build AI Agents and Multi-Agent Systems with CrewAI | Notes
大模型基础,毛玉仁等 - 2024,浙大
System Design Interview, An Insider's Guide, Second Edition - by Alex Xu 2020
Educative - Grokking System Design Interview | PDF Notes | Markdown Notes
Educative - Grokking the Modern System Design Interview | Markdown Notes
计算机底层的秘密,陆小风 - 2023,电子工业出版社
BDAI Conference, 2024 Oct 3-5, UChicago
C++ Design Patterns and Derivatives Pricing (Mathematics, Finance and Risk, Series Number 2) 2nd Edition, by M. S. Joshi
An Introduction to High-Frequency Finance, by Ramazan Gençay, et al.
Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition Paperback – by Stefan Jansen 2020
Stochastic Volatility Modeling (Chapman and Hall/CRC Financial Mathematics Series) 1st Edition, by Lorenzo Bergomi
Quant Job Interview Questions and Answers (Second Edition) – by Mark Joshi 2013
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AI-LLM-ML-CS-Quant-Overview is a repository providing overview notes on AI, Large Language Models (LLM), Machine Learning (ML), Computer Science (CS), and Quantitative Finance. It covers various topics such as LangGraph & Cursor AI, DeepSeek, MoE (Mixture of Experts), NVIDIA GTC, LLM Essentials, System Design, Computer Systems, Big Data and AI in Finance, Econometrics and Statistics Conference, C++ Design Patterns and Derivatives Pricing, High-Frequency Finance, Machine Learning for Algorithmic Trading, Stochastic Volatility Modeling, Quant Job Interview Questions, Distributed Systems, Language Models, Designing Machine Learning Systems, Designing Data-Intensive Applications (DDIA), Distributed Machine Learning, and The Elements of Quantitative Investing.
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