GitHub - finxter/AI-Engineering-Learning-Resources (original) (raw)

The Best AI Engineering Learning Resources (Curated List)

AI engineering is THE ULTIMATE SKILL in the age of AI - because as AI engineers we deploy models in the real world. AI - no matter how powerful - would be useless without AI engineers!

The goal of this resource guide is to help you deepen your understanding of AI engineering, prompting, and related fields. I have curated these resources from various GitHub repositories, Reddit, X, and other online sources.

If you like stuff like this, also check out my free email newsletter to help you be on the right side of change. More than 150,000 upcoming AI engineers are already subscribed - and counting!

Courses and Guides

• AI Engineering Academy - ⭐ Full Program to becoming an AI engineer, affordable with downloadable course certificates.
• ChatGPT Prompt Engineering for Developers! - A comprehensive set of courses by OpenAI employees.
• Learn Prompting - Courses focused on prompt engineering and AI engineering.
• Prompt Engineering by Lilian Weng - An in-depth post from Lilian Weng, Head of Applied AI Research at OpenAI.
• MLOps Guide - A guide on MLOps best practices.
• MLOps Zoomcamp - A comprehensive course on MLOps.
• Prompt Engineering Repository - Covers the history, strategies, guidelines, and safety recommendations for working with LLMs.
• Gandalf - A fun tool to learn about prompt injection.
• Practical Deep Learning - A course for applying deep learning and machine learning to practical problems.
• Let's Build GPT - ⭐ From Andrej Karpathy, building GPT from scratch in code.
• AI Canon - ⭐ A curated list of essential AI resources.
• Generative AI Learning Path - ⭐ A learning path on Generative AI products and technologies from Google Cloud.
• Rules of Machine Learning - Google's best practices in machine learning.
• AI Companion App - A tutorial stack to create and host AI companions accessible via browser or SMS.
• Transformers - Introduction to Transformers with Andrej Karpathy.
• The Illustrated Transformer - A great visual explanation of the Transformer architecture.

⭐ FREE Course: A Beginner's Guide to AI Engineering (Full Course)

List of AI Model Resources

• ChatGPT - A conversational AI developed by OpenAI capable of answering questions, writing content, solving math problems, visualizing data, coding, image generation, web search, and assisting with a wide variety of tasks in natural language.
• HuggingChat - ⭐ Making the community's best AI chat models available to everyone.
• Venice AI - Uncensored AI Model
• GPT-4o - High-intelligence flagship model for complex, multi-step tasks
• GPT-4o-mini - Affordable and intelligent small model for fast, lightweight tasks
• o1-preview and o1-mini - Language models trained with reinforcement learning to perform complex reasoning.
• Open LLM Leaderboard Evaluate and rank open-source LLMs.
• DemoGPT - Create 🦜️🔗 LangChain apps using prompts.
• Llama2 Web UI - Run Llama 2 with a Gradio web UI on GPU or CPU.
• Code Llama - A specialized variant of Meta's LLaMA model tailored for programming tasks, offering enhanced capabilities for code generation and understanding.
• JARVIS - An interface to connect numerous AI models.
• Flan - A series of large language models by Google trained with fine-tuning on a variety of tasks to improve general language understanding and response accuracy.
• Gemini - A family of models from DeepMind, designed to offer conversational abilities similar to ChatGPT, with enhancements focused on reasoning and problem-solving.
• OpenAI Flagship Models (GPT-4o and o1) - OpenAI's latest and most powerful language model, capable of understanding and generating complex content, used in many applications, including the latest versions of ChatGPT.
• LLaMA - Developed by Meta, this large language model focuses on efficiency and performance, designed to make advancements in natural language processing accessible to researchers.
• Mixtral - A model combining fine-tuned capabilities from Mistral AI, focusing on enhancing conversational and generative performance.
• Phi-2 - An experimental language model exploring new architectures and learning techniques, aimed at advancing the field of NLP with a focus on energy efficiency and interpretability.

Agents and Agent Frameworks

• Open-Assistant An open-source chat agent interacting with external sources.
• Auto-GPT An experimental open-source attempt to make GPT-4 fully autonomous.
• LoopGPT ⭐ A modular reimplementation of Auto-GPT.
• ThinkGPT Implements Chain of Thought reasoning for LLMs.
• Transformers Agents ⭐ Provides a natural language API on top of transformers with curated tools.
• GPT-Engineer Specify requirements, and the AI builds the project.
• Khoj An AI personal assistant for your digital brain.
• Danswer Open-source enterprise question-answering.
• simpleaichat Python package for interfacing with chat apps.
• RealChar A realistic character chatbot.
• MetaGPT Multi-Agent Framework for generating PRDs, designs, tasks, and repositories.
• ChatGPT AutoExpert Custom instructions for ChatGPT and advanced data analysis.

Development Frameworks and Libraries

• LangChain ⭐ Framework for developing applications powered by LLMs.
• Semantic Kernel SDK for integrating LLMs with conventional programming languages.
• Langcorn API server for serving LangChain models and pipelines.
• Pinecone Vector database for long-term memory with models.
• Chroma Open-source alternative to Pinecone.
• Plugandplai Simplify plugin integration into open-source LLMs.
• GPTCache Caching for LLM responses to save costs.
• OpenAI Cookbook Examples and best practices for building with OpenAI.
• How to Build an Agent with LangChain Jupyter notebook for building agents with LangChain.
• Mojo A programming language bridging research and production.
• smol developer ⭐ Your own personal junior developer.
• smol plugin Automatically generate OpenAI plugins from API specifications.
• Kor A thin wrapper for extracting structured data from LLMs.
• tiktoken Fast BPE tokenizer used with OpenAI's models.
• OpenAI Function Calling Standardize LLM output.
• Vercel AI SDK Build AI-powered applications with React, Svelte, and Vue.
• Code Interpreter API Open-source implementation of the ChatGPT Code Interpreter.
• Open Interpreter Locally running implementation of OpenAI's Code Interpreter.

Tools

• Vault AI Upload documents and ask questions about their content.
• privateGPT ⭐ Document Q&A using open-source LLMs.
• Quivr ⭐ Your Generative AI second brain for files and thoughts.
• h2oGPT ⭐ Similar to privateGPT with GPU inference support.
• localGPT Uses Vicuna-7b and InstructorEmbeddings with GPU/CPU support.
• rag-stack Deploy a private ChatGPT alternative within your VPC.
• LangFlow ⭐ Visual prototyping and experimentation with LangChain.
• Flowise Similar to LangFlow but with LangChainJS.
• Stable Diffusion Web UI Browser interface for Stable Diffusion based on Gradio.
• Unofficial OpenAI Status ⭐ In-depth OpenAI status page.
• PentestGPT 🕵️ GPT-powered penetration testing tool.
• TypingMind ⭐ Enhanced UI for ChatGPT.
• Dify Create and operate AI-native apps based on OpenAI GPT models.
• txtai Semantic search and workflows powered by language models.

Prompting Techniques

• Zero-Shot Prompting
• Few-Shot Prompting
• Chain-of-Thought Prompting
• Self-Consistency
• Generate Knowledge Prompting
• Prompt Chaining
• Tree of Thoughts (ToT)
• Retrieval Augmented Generation
• Automatic Reasoning and Tool-use (ART)
• Automatic Prompt Engineer
• Active-Prompt
• Directional Stimulus Prompting
• Program-Aided Language Models
• ReAct Prompting
• Multimodal CoT Prompting
• Graph Prompting

AI Engineering Papers

• Prompt Engineering - Papers

Contributing

Contributions are welcome! Please subscribe to my free email newsletter on AI - and send me your suggestions as a reply to my welcome email so I know you're not a bot. 😉

License

MIT