This repository provides a collection of minimal, reusable, and extendable machine learning notebooks covering various tasks and applications. It is designed for educational and research purposes, offering practical implementations of fundamental ML concepts and state-of-the-art techniques.

How It Works

The notebooks are structured by ML domain, including foundational concepts, Natural Language Processing (NLP), Transformers, Computer Vision, Generative Adversarial Networks (GANs), and Parameter-Efficient Fine-Tuning (PEFT). Each notebook focuses on a specific task, demonstrating core algorithms and model architectures with clear explanations and code. The project emphasizes practical application and ease of understanding for learners.

Quick Start & Requirements

• Installation: Supports GitHub Codespaces for an immediate, pre-configured environment. Alternatively, clone the repository and set up a Conda environment using the provided spec-file.txt.
• Prerequisites: Python, Conda, PyTorch. Specific dependencies are listed in spec-file.txt.
• Resources: Codespaces setup is described as quick. Local setup requires Conda and Python.
• Links: GitHub Repository

Highlighted Details

• Comprehensive coverage from basic computational graphs and linear regression to advanced topics like Transformers, GANs, and PEFT methods (LoRA, QLoRA).
• Includes implementations for various NLP tasks (text classification, NER, question answering) and Computer Vision tasks (object detection, image similarity).
• Features modern techniques like Attention Mechanisms, Positional Embeddings, and various GAN architectures.
• Dedicated section on Parameter-Efficient Fine-Tuning for large language models like BERT, T5, TinyLlama, and Mistral.

Maintenance & Community

The project is maintained by dair-ai. Users are encouraged to open issues for bugs or questions. Contact is available via Twitter.

Licensing & Compatibility

The repository is available for educational and research purposes. Specific licensing details are not explicitly stated in the README, but the usage is restricted to non-commercial applications.

Limitations & Caveats

The README explicitly states the notebooks are for educational and research purposes, implying potential limitations for direct commercial or production use without further adaptation or licensing clarification.