AI Jupyter Notebooks

🔖 About This Project

TL;DR Learn the basics of AI on your local machine to solve real-world problems with step-by-step guides through interactive notebooks 🤖.

This repo consists of a series of Jupyter notebooks explaining how to solve problems through using various methods of AI. We use Pytorch and scikit-learn to train and evaluate AI models on real data obtained from various sources. Each notebook is a step-by-step guide of how to solve a given problem with in-depth descriptions of the supporting math and code.

This project is part of my broader goal to create tutorials and resources for understanding and using AI on local machines 💻. For other in depth tutorials pertaining to building agents and the local tool servers to power them, check it out here.

Now, let's get building!

🏁 Getting Started

1. Clone the repo, head there, then create a Python environment:

   git clone https://github.com/anima-kit/ai-notebooks.git
   cd ai-notebooks
   python -m venv venv

2. Activate the Python environment:

   venv/Scripts/activate

3. Install the necessary Python libraries:

   pip install -r requirements.txt

4. Now you can run any of the Jupyter notebooks included in the repo.

📝 Example Use Cases

I plan on dividing tutorials into three main aspects of AI: Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL). Tutorials will be released periodically starting with the basics of ML which can be found in the ./00-ML folder.

The first tutorials will be a brief introduction to Supervised Learning (SL) as well as deep dives into the one of the simplest SL algorithms - linear regression. We use a Kaggle dataset to train and evaluate a model to predict the total energy consumed given various features, and we learn techniques for improving our model which we can carry over to other learning methods.

📚 Next Steps & Learning Resources

This project is part of a series on learning and using AI. For a deeper dive, check out my tutorials. Topics include:

• Setting up local servers to power AI agents
• Example agent workflows (simple chatbots to specialized agents)
• Implementing complex RAG techniques
• Discussing various aspects of AI beyond agents (like these notebooks)

Want to learn AI? Visit my portfolio to explore more tutorials and projects!

🏯 Project Structure

├── 00-ML/                          # Folder containing all ML notebooks
│   └── 00-regression/              # Folder containing all regression notebooks
│       └── 00-basics.ipynb         # 1st tutorial for regression basics
│       └── 01-preprocessing.ipynb  # 2nd tutorial for regression basics
├── requirements.txt                # Required Python libraries for main app

⚙️ Tech

• Jupyter: For interactive notebooks
• Pytorch: For building and evaluating AI models
• scikit-learn: For building and evaluating AI models
• Kaggle: For obtaining various datasets

🔗 Contributing

This repo is a work in progress. If you'd like to suggest or add improvements, fix bugs or typos etc., feel free to contribute. Check out the contributing guidelines to get started.