KEY LEARNINGS
- Deep learning enables automatic feature discovery, eliminating the need for manual feature engineering.
- The 2012 AlexNet breakthrough demonstrated the power of deeper neural networks.
- Training deep learning systems requires massive computational resources, concentrating AI development among well-funded organizations.
- Deep learning systems excel on benchmarks but may fail in real-world deployment scenarios.
- Governance must account for the probabilistic nature and inherent opacity of deep learning systems.
- 📄Deep Learning Book (Goodfellow et al.)Comprehensive textbook on deep learning fundamentals.
- 🎥3Blue1Brown: Neural NetworksVisual explanations of neural network concepts.
- 🔧Papers With CodeDatabase of ML papers with implementation code.
- Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. NeurIPS.
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature.
- Silver, D., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature.
