# [SCI] Deep Learning
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**Deep Learning** is the application of deep (many-layer) neural networks to perception and prediction tasks, achieving human or superhuman performance on image recognition, speech, language, and scientific modelling.
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## Overview
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The ImageNet breakthrough (Krizhevsky, Sutskever, Hinton, 2012) demonstrated that deep convolutional networks trained on GPUs massively outperformed all prior approaches for image classification. This launched the modern AI era. Transformers (Vaswani et al., 2017) replaced recurrent networks for sequence modelling, enabling GPT (2018), BERT (2018), and ultimately GPT-4 (2023) and Claude. AlphaFold (DeepMind, 2020) solved the 50-year protein structure prediction problem. Deep learning is now applied to drug discovery, materials design, climate modelling, and physics.
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## Key Figures & Recognition
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- **Geoffrey Hinton** (1947–), **Yann LeCun** (1960–), **Yoshua Bengio** (1964–): **Turing Award 2018**. Hinton: **Nobel Prize in Physics 2024**.
- **Ilya Sutskever** (1985–): GPT, co-founded OpenAI.
- **Demis Hassabis** (1976–): AlphaFold, AlphaGo. **Nobel Prize in Chemistry 2024**.
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## Seminal Papers
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- [Krizhevsky, A., Sutskever, I. & Hinton, G. "ImageNet Classification with Deep CNNs." *NeurIPS* (2012)](https://dl.acm.org/doi/10.5555/2999134.2999257)
- [Vaswani, A. et al. "Attention Is All You Need." *NeurIPS* (2017)](https://arxiv.org/abs/1706.03762)
- [Jumper, J. et al. (DeepMind). "Highly accurate protein structure prediction with AlphaFold." *Nature* (2021)](https://doi.org/10.1038/s41586-021-03819-2)
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# Parents
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* [SCI] Machine Learning Theory⏎