This repository serves as a curated "journal" of resources for learning Artificial Intelligence, targeting individuals who prefer a self-directed, practical approach. It offers a comprehensive collection of books, courses, videos, tools, models, datasets, and papers, aiming to guide learners through the rapidly evolving AI landscape by sharing the author's personal learning journey and recommended materials.

How It Works

The repository functions as a personal learning log, organized by resource type rather than a strict curriculum. It emphasizes hands-on learning and practical application, recommending resources that balance theoretical understanding with actionable implementation. The author's approach is to learn by building and exploring, sharing materials that have proven useful in their own AI development journey.

Quick Start & Requirements

This repository is a collection of links and recommendations, not a runnable software project. No installation or specific requirements are needed to browse its contents.

Highlighted Details

• Extensive curated list of books covering AI engineering, LLMs, deep learning, data science, and AI ethics.
• Includes recommendations for courses and video tutorials from institutions like MIT and creators like Andrej Karpathy.
• Features a categorized list of essential AI tools, frameworks (Hugging Face, Ollama), and Python libraries (TensorFlow, PyTorch).
• Provides links to popular models (GPT, LLaMA), model hubs, and datasets for practical exploration.

Maintenance & Community

The repository is maintained by ArturoNereu. There are no explicit mentions of community channels, partnerships, or a public roadmap.

Licensing & Compatibility

The repository itself contains links to external resources, and the licensing of those resources would vary. The content within this repository is shared for educational purposes.

Limitations & Caveats

This repository is a personal learning journal and not a structured curriculum, which may require users to synthesize information independently. Some book recommendations are dated (e.g., referencing 2025), and the focus is heavily on Python-centric tools and libraries.