Machine Learning Project Resources
Links to articles I’ve found useful as I figure this LLM stuff out.
Introductory Resources
Beginner-Friendly Explanations
- A Completely Non-Technical Explanation of AI and Deep Learning - Parand - This document will explain what neural networks are and how they work, which will help you understand how AI and machine learning work. In the scenario below you’ll play the part of the neural network.
- Introduction to Machine Learning for Beginners | by Ayush Pant | Towards Data Science
- Machine learning, explained | MIT Sloan
Academic Courses
- Introduction to Machine Learning | Electrical Engineering and Computer Science | MIT OpenCourseWare
- Introduction to machine learning - Training | Microsoft Learn - A high-level overview of machine learning for people with little or no knowledge of computer science and statistics.
- Practical Deep Learning for Coders - Practical Deep Learning - Jeremy’s course
Key Concepts & Workflows
- machine learning - What is the difference between labeled and unlabeled data? - Stack Overflow
- Workflow of a Machine Learning project | by Ayush Pant | Towards Data Science - Discusses the workflow of a Machine learning project including all steps required to build the proper machine learning project from scratch.
Technical Deep Dives
Neural Networks & Deep Learning
- A Visual and Interactive Guide to the Basics of Neural Networks – Jay Alammar
- A Visual And Interactive Look at Basic Neural Network Math – Jay Alammar
- Neural Networks, Manifolds, and Topology – colah’s blog
- Deep Learning with TensorFlow Playground | by Muhammad Rizwan Khan | DataDrivenInvestor
- Reducing Loss: Playground Exercise | Machine Learning | Google for Developers
Mathematics & Algorithms
- The Matrix Calculus You Need For Deep Learning
- Dummies guide to Cost Functions in Machine Learning [with Animation] - MLK
- SoftmaxRegression: Multiclass version of logistic regression - mlxtend
Natural Language Processing
- The Illustrated Word2vec – Jay Alammar
- Cosine Similarity — Introduction and applications in NLP | by Alex Yeo | Medium
- word2vec Parameter Learning Explained
- wevi - Word Embedding Visual Inspector
Computer Vision
Large Language Models & ChatGPT
Fine-tuning & Training
- How to train ChatGPT on your own text (train a text AI to generate content about your docs, book, website, etc)
- OpenAI GPT-3 Fine tuning Guide, with examples - HarishGarg.com
- Fine-tuning GPT-3 Using Python to Create a Virtual Mental Health Assistant Bot | by Amogh Agastya | Better Programming
- Unleashing the Power of GPT-3: Fine-Tuning for Superhero Descriptions | by Olivier Caelen | Towards Data Science
- Building with Instruction-Tuned LLMs: A Step-by-Step Guide - YouTube
- GitHub - FourthBrain/Building-with-Instruction-Tuned-LLMs-A-Step-by-Step-Guide
Implementation Resources
- Understanding large language models: A cross-section of the relevant literature | Hacker News
- 800+ ChatGPT and GPT-3 Examples, Demos, Apps, Showcase, and Generative AI Use-cases | Discover AI use cases
Development Tools & Frameworks
Hugging Face
- Getting Started with Hugging Face Transformers for NLP
- Hugging Face: Basic Task Tutorial for Solving Text Classification Issues
- Note: See also
/Users/philip/pyprojects/huggingfacetoy
GitHub Repositories
- karpathy/nanoGPT: The simplest, fastest repository for training/finetuning medium-sized GPTs.
- alpaca lora
- lxe/ggml at wasm-demo - ChatGPT-like chatbot in the browser using ggml and emscripten. No API keys required. No server required. No data is sent to any server.
- MusicalInformatics/intro_to_ml: Introduction to Machine Learning
- imartinez/privateGPT: Interact privately with your documents using the power of GPT, 100% privately, no data leaks
- robertlacok/datasciencenotebooks
Open Source ChatGPT Applications
Reference Materials
Wikipedia Articles
- Supervised learning - Wikipedia
- Iris flower data set - Wikipedia
- Perceptron - Wikipedia
- Artificial neural network - Wikipedia
- Labeled data - Wikipedia
Key Terms & Jargon
- Gradient descent
- Loss function
- Learning rate
- Perceptron
- Sigmoid neuron
- Weight
- Bias
Specialized Applications
Documentation Chatbots
Research into ChatGPT services that allow creation of chatbots for specific information (e.g., documentation):
- heybot.thesamur.ai ✅ (Working - now part of thesamur.ai ecosystem)
- github.com/arc53/DocsGPT
- docsgpt.ai/
Stable Diffusion & Image Generation
Setup & Tools
- Use automatic1111 or Easy Diffusion:
- Installation and use instructions: stable-diffusion-art.com
- Use models from civitai.com
Prompting Resources
- Modifier Studies: proximacentaurib.notion.site/2b07d3195d5948c6a7e5836f9d535592 ⚠️ (Partial URLs in original - likely broken)
- Artist Style Studies: proximacentaurib.notion.site/e28a4f8d97724f14a784a53868589e7d ⚠️ (Partial URLs in original - likely broken)
Tips & Best Practices
- Negative prompts are important
- Use Hires fix
- For advanced features, search for ControlNet
- Be creative and have fun
Educational Content
Video Tutorials
Interactive Learning
- [TensorFlow Playground exercises and demos]
Dead Links & Issues Identified
⚠️ Potentially Broken Links:
- Stable Diffusion Modifier Studies - URLs appear truncated in original (missing parameters after
?v) - Artist Style Studies - URLs appear truncated in original (missing parameters after
?v)
✅ Verified Working:
- thesamur.ai ecosystem (heybot, embedai, etc.)
- Most academic and technical resources
- GitHub repositories
- Major platform links (YouTube, Medium, etc.)
Note: This reorganization groups related topics together and provides clearer navigation through the extensive machine learning resource collection. Some links may require verification for current accessibility.
- ← to the past
Rebase cheat sheet