Deep Learning
Deep learning extends neural networks with multiple layers to learn hierarchical representations. These architectures have achieved state-of-the-art results in computer vision, speech recognition, and natural language processing.1
Topics in This Section
| Topic | Description |
|---|---|
| CNNs | Convolutional neural networks for computer vision |
| RNNs & LSTMs | Recurrent architectures for sequential data |
| Autoencoders | Unsupervised representation learning |
| GANs | Generative adversarial networks |
Learning Path
CNNs → RNNs & LSTMs → Autoencoders → GANs
Related Domains
- Need fundamentals first? See 01 - AI Fundamentals
- Moving to language models? See 04 - NLP or 05 - Generative AI
- Interested in RL? See 06 - Reinforcement Learning
References
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539 ↩︎