Resources

Andrew Ng's comprehensive course covering neural networks, deep learning, and practical applications on Coursera.

Top-down, practical approach to deep learning. Learn to train models before diving into theory.

Harvard's introduction to AI covering search, knowledge, uncertainty, optimization, and machine learning.

Classic machine learning course covering supervised learning, unsupervised learning, and best practices.

Learn how to use transformers for NLP tasks with practical examples and the Hugging Face ecosystem.

The foundational paper introducing the Transformer architecture that powers modern LLMs.

Introduced bidirectional pre-training for language understanding, revolutionizing NLP benchmarks.

Demonstrates that scaling language models leads to emergent few-shot learning capabilities.

Introduced skip connections enabling training of very deep networks, fundamental to modern architectures.

Anthropic's approach to training AI systems to be helpful, harmless, and honest.

Open-source deep learning framework known for dynamic computation graphs and research flexibility.

Google's production-ready ML platform with comprehensive tools for model building and deployment.

Library providing thousands of pre-trained models for NLP, vision, and audio tasks.

Framework for developing applications powered by language models with composable components.

ML experiment tracking, dataset versioning, and model management platform.

Run large language models locally on your machine with simple setup.

Large-scale image database organized by WordNet hierarchy, foundational for computer vision research.

Petabytes of web crawl data used for training many large language models.

Library providing access to thousands of datasets for ML research and development.

Community-contributed datasets covering diverse domains and problem types.

800GB diverse text dataset designed for training large language models.

Active Reddit community discussing research papers, industry news, and ML projects.

Various Discord communities for real-time discussion, including EleutherAI and Hugging Face.

Track ML papers with code implementations, datasets, and benchmark results.

Follow researchers and practitioners sharing insights, papers, and discussions.

Comprehensive textbook covering the mathematical and conceptual foundations of deep learning.

Practical guide to ML using Python tools, great for learning by doing.

Bishop's classic text on statistical pattern recognition and machine learning theory.

Concise overview of ML concepts, perfect for getting a quick foundation.

Practical guide to NLP using Hugging Face Transformers library.