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AI Study Group

Last updated: 2/16/2026

TL;DR

If you only have limited time to learn Artificial Intelligence, here’s what I recommend:

If you want more (and there’s a lot more) keep reading.

Why this repo exists

Learning often feels like walking down a road that forks every few meters; you’re always exploring, never really arriving. And that’s the beauty of it.

When I was working in games, people would ask me: “How do I learn to make games?” My answer was always: “Pick a game, and build it, learn the tools and concepts along the way.” I’ve taken the same approach with AI.

This repository is a collection of the material I’ve used (and continue to use) to learn AI: books, courses, papers, tools, models, datasets, and notes. It’s not a curriculum, it’s more like a journal. One that’s helped me build, get stuck, and keep going.

Do I know AI? Not really. But I’m learning, building, and having a great time doing it.

I hope something in here is useful to you too. And if you have suggestions or feedback, I’d love to hear it.

Books

NI Cool

Here are my top 5 recommended books to make sense of AI/ML/DL:

  • AI Engineering: Building Applications with Foundation Models If you feel lost and don't know where to start, this book can serve as a great map. Chip explains the most common concepts behind AI in a clear and approachable way.
  • Hands-On Large Language Models: Language Understanding and Generation Building an LLM from scratch is difficult, even understanding existing open-source options can be challenging. This book does a great job of explaining how LLMs work and introduces common architectures at a deep enough level to be practical without being overwhelming.
  • Deep Learning - A Visual Approach Probably the best resource out there for building solid intuition about the many concepts surrounding deep learning. Andrew, the author, did a wonderful job illustrating these concepts, making it much easier to develop a real understanding of them.
  • Deep Learning The most complete and in-depth book on Deep Learning. It's dense but thorough, covering the math, statistics, and core algorithms that form the foundation of modern AI. It helped me build a clearer mental model of the tools used in research and implementation.
  • Practical Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD This book comes with video lectures by Jeremy Howard, the author and creator of FastAI. The goal is to give coders a head start in deep learning without diving too deep into the weeds.

See the full list of books

Courses & Tutorials

AI Teacher

Not everyone learns the same way, sometimes I get too tired of just reading, and tutorials or courses in video form make me feel like I’m talking to someone. If you prefer learning through videos or more interactive formats, I recommend taking a look at the following materials:

  • Getting Started with Deep Learning A short introduction to deep learning, delivered by NVIDIA. If you just want a quick glimpse of the very basics before jumping into higher-level implementation, this is a solid place to start.
  • MIT Intro to Deep Learning A free, intensive bootcamp taught by MIT researchers. Getting direct access to this content (updated every time they teach it) is amazing. (~10 hours of deep learning concepts, plus interesting guest lectures.)
  • Practical Deep Learning for Coders This is the accompanying course version of the FastAI book by Jeremy Howard.
  • C++ Neural Network in a Weekend This might be too much if you’re just starting out, or if you’re not interested in low-level C++ implementations of neural networks. But I found it fascinating, and it made me appreciate how far modern frameworks and APIs have come.
  • 🤗 Agents Course Now that agentic AI is trending, Hugging Face launched this free course showcasing their smolagents framework. It also covers LlamaIndex and LangGraph.

Videos & Talks

I find it increasingly difficult these days to stay focused on videos. Maybe it’s because watching usually means being on a computer or phone, and distractions are always just a click away.
However, the videos listed here are so well-made, well-researched, and genuinely interesting that I believe they’re not just useful, but sticky. One more thing I appreciate: in the world of AI, many of the best video talks and tutorials often come directly from the people actually building the models, frameworks, and tools. What a great time to be learning.

Tools, Frameworks & Platforms

AI Architect

Understanding all the tools, frameworks, architectures, and ecosystems around AI can sometimes feel harder than understanding AI itself. Below are the ones I’ve explored and used enough to feel confident recommending.
Of course, these won’t solve every use case, and I’m not listing every supporting technology you might need to build real-world AI systems, but it’s a start.

Category Tools
Core Frameworks - Hugging Face: A hub, that hosts models, datasets, apps, and communities around AI.
- Ollama: Run LLMs locally in your computer(CLI).
- LM Studio: Discover, and run LLMs in your computer, using a UI.
Developer Tools - Gradio: Create ML-powered apps for the web. Easy to use UI API.
- Google Colab: You have seen probably many resources use Jupyter Notebooks, this platform allows you to run them.
- MongoDB: Database that allows you to perform vector search.
Visualization Tools - LLM Visualization: Visualize, and see step-by-step how an LLM works.
- Embeddings Vis: Visualize the resulting embeddings from multiple models, and their semantic distance. In 3D space.
Platforms - Lambda: Computing resources for rent for training and inferencing.

Python Libraries & Toolkits

AI Aha

AI goes far beyond any single language, operating system, hardware, or framework. There are countless implementations across different programming languages, runtimes, and platforms. From my experience, though, Python is what most people use and teach.
Following that path, I’ve focused most of my learning around Python as well. That said, similar libraries (and often the same ones) likely exist for your favorite environment too.

Category Libraries
Data Science & Computation Pandas, NumPy, SciPy, scikit-learn
Plotting & Visualization Matplotlib, Seaborn
Machine Learning / Deep Learning TensorFlow, PyTorch
Image Processing Pillow
Web Scraping Beautiful Soup, Selenium

Models

NI Wee

At the core of deep learning are the models. Some are general-purpose large language models (LLMs), while others are specialized for specific tasks like text generation, image creation, or coding.

These are the models I’ve used or explored:

If you're looking to explore beyond these, I recommend checking out the following model hubs. They host a wide variety of models with different licenses and for many use cases:

Articles, Blogs & Interviews

Papers

AI Scientist

At the core of most AI advances is research — deep, complex work published by people pushing the boundaries of what’s possible. These breakthroughs often appear in the form of academic papers. Reading papers can be overwhelming at first. It’s not always easy to unpack their meaning or follow the math. I suggest using tools like NotebookLM or joining a local AI paper-reading club.

Read the list here

Datasets

AI Question

The current approach to teaching machines relies heavily on data — often, massive amounts of it. In some cases, we use datasets that were created and labeled by humans. In others, we rely on synthetic data generated by machines, or a combination of both. This section includes some well-known datasets you can explore and use to train your models. Platforms like Hugging Face also host a wide range of datasets for different tasks and domains.

A good dataset to start with, is 🍷 FineWeb. Used to train nanochat, among other models.

Name Domain
Kaggle Datasets Various / General ML
CelebA Computer Vision / Facial Attributes
COCO Computer Vision / Object Detection
ImageNet Computer Vision / Classification
Cityscapes Dataset Computer Vision / Segmentation
ObjectNet Computer Vision / Robustness Testing
LAION 5B Multimodal / Vision-Language
NAIRR Datasets Various / Research Datasets
UCI Machine Learning Datasets Traditional ML / Tabular
Common Crawl NLP / Web-Scale Corpus
The Pile NLP / Language Modeling
C4 (Colossal Clean Crawled Corpus) NLP / Pretraining Corpus

Notes & Highlights

AI Sleeping