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.
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.
Here are my top 5 recommended books to make sense of AI/ML/DL:
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:
smolagents framework. It also covers LlamaIndex and LangGraph.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.
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. |
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 |
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:
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.
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 |
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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.
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.
Here are my top 5 recommended books to make sense of AI/ML/DL:
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:
smolagents framework. It also covers LlamaIndex and LangGraph.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.
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. |
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 |
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:
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.
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 |
157 followers · starred Jun 2025
127 followers · starred Apr 2026
0 followers · starred May 2026
8 followers · starred Jun 2026