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Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.

This is a workshop designed for Amazon Bedrock a foundational model service.

New ways of breaking app-integrated LLMs

Repository containing notebooks of my posts on Medium

《面向开发者的 ChatGPT 提示词工程》非官方版中英双语字幕 Unofficial subtitles of "ChatGPT Prompt Engineering for Developers"

Various Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau

Developer Asset Hub for NVIDIA Nemotron — A one-stop resource for training recipes, usage cookbooks, datasets, and full end-to-end reference examples to build with Nemotron models

Implementation of all RL algorithms in a simpler way

Hands-On Machine Learning for Algorithmic Trading, published by Packt

Natural Gradient Boosting for Probabilistic Prediction

A comprehensive guide to building RAG-based LLM applications for production.

Python library for interactive topic model visualization. Port of the R LDAvis package.

Practical course about Large Language Models.

WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

A comprehensive guide to understanding and implementing large language models with hands-on examples using LangChain for GenAI applications.

fastquant — Backtest and optimize your ML trading strategies with only 3 lines of code!

Machine Learning in Asset Management (by @firmai)

Code for the paper "ViperGPT: Visual Inference via Python Execution for Reasoning"

Synthetic data generators for tabular and time-series data

KoAlpaca: 한국어 명령어를 이해하는 오픈소스 언어모델 (KoAlpaca: An open-source language model to understand Korean instructions)

The official code repository for the second edition of the O'Reilly book Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play.

Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph. This is the companion repository for the book on generative AI with LangChain.

Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy

Python implementation of CMA-ES

The repository for all Azure OpenAI Samples complementing the OpenAI cookbook.

Instructional notebooks on music information retrieval.

Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics

code for deep learning courses

LangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading and indexing data, creating prompt templates, CSV agents, and using retrieval QA chains to query the custom data. Projects for using a private LLM (Llama 2) for chat with PDF files, tweets sentiment analysis.