A curated collection of Deep Learning resources, from foundational concepts to advanced topics.
Foundational Courses
Deep Learning Specialization by Andrew Ng A comprehensive series covering neural networks, CNN, RNN, and practical aspects of deep learning projects.
Fast.ai Course Practical Deep Learning for Coders - focuses on hands-on implementation using PyTorch.
MIT 6.S191 MIT’s introductory course to deep learning methods with applications.
Books
Deep Learning by Goodfellow, Bengio, and Courville The comprehensive “bible” of deep learning, covering both theory and practical aspects.
Dive into Deep Learning An interactive deep learning book with code, math, and discussions.
Frameworks and Libraries
PyTorch
- Official PyTorch Tutorials
- PyTorch Lightning - Lightweight PyTorch wrapper for high-performance AI research
- Torchvision - Computer vision packages
TensorFlow
- TensorFlow Documentation
- Keras - High-level neural network library
- TensorFlow Model Garden - Collection of state-of-the-art models
Research Papers and Implementations
Computer Vision
- ResNet - Deep Residual Learning
- Vision Transformer (ViT) - Transformers in Vision
- YOLO - Real-Time Object Detection
Natural Language Processing
- Transformer - Attention Is All You Need
- BERT - Pre-training of Deep Bidirectional Transformers
- GPT - Language Models are Few-Shot Learners
Practical Resources
Datasets
Model Zoo
Advanced Topics
Optimization and Training
Architecture Design
Community and News
- r/MachineLearning
- Papers with Code
- Distill.pub - Clear explanations of machine learning concepts
- ML Subreddit