Outline of deep learning

This article contains dynamic lists that may never be able to satisfy particular standards for completeness. You can help by editing the page to add missing items, with references to reliable sources.

The following outline is provided as an overview of, and topical guide to, deep learning:

Deep learning is a subfield of machine learning and artificial intelligence based on artificial neural networks with multiple processing layers. It emphasizes representation learning and is widely used in areas such as computer vision, natural language processing, speech recognition, recommender systems, robotics, and generative artificial intelligence.

Ways to categorize deep learning

History

Precursors

Milestones

Core concepts

Learning settings

Common tasks

Architectures

Feedforward and convolutional architectures

Recurrent and sequence architectures

Representation-learning architectures

Attention and transformer architectures

Generative and probabilistic architectures

Graph and memory architectures

Neural network components and techniques

Training and optimization

Datasets and benchmarks

Applications

Computer vision

Natural language processing

Speech and audio

Science and medicine

Robotics and control

Recommendation, search, and forecasting

Generative artificial intelligence

Computer graphics and video games

Hardware

Supporting software platforms

Software

Open-source frameworks and libraries

Neural network software

Platforms, tools, and deployment

Algorithms for deep learning and neural networks

Representation and metric learning

Generative modeling

Efficient and scalable deep learning

Reliability, safety, and interpretability

Conferences and workshops

Organizations

Research laboratories and institutions

Companies

Publications

Books

Journals

Influential persons

See also

References

  1. ↑ LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015-05-27). "Deep learning". Nature. 521 (7553): 436–444. Bibcode:2015Natur.521..436L. doi:10.1038/nature14539. PMID 26017442.
  2. ↑ Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). Deep Learning. The MIT Press. ISBN 978-0-262-03561-3.
  3. ↑ Schmidhuber, Jürgen (January 2015). "Deep learning in neural networks: An overview". Neural Networks. 61: 85–117. arXiv:1404.7828. Bibcode:2015NN.....61...85S. doi:10.1016/j.neunet.2014.09.003. PMID 25462637.
  4. ↑ Biggs, David; Nuttall, Andrew (2015). Neural Memory Networks (PDF) (Report). CS229 Final Report. Stanford University. Retrieved 17 April 2026.
  5. ↑ Akash Ajagekar (2021). "Adam". Cornell University Computational Optimization Open Textbook – Optimization Wiki. Retrieved 17 April 2026.
  6. ↑ "COCO: Common Objects in Context". COCO: Common Objects in Context. Retrieved 17 April 2026.
  7. ↑ "GLUE Benchmark". GLUE Benchmark. Retrieved 17 April 2026.
  8. ↑ "LibriSpeech ASR corpus". Open Speech and Language Resources. Retrieved 17 April 2026.
  9. ↑ "LibriSpeech-Long". GitHub. Google DeepMind. 2024. Retrieved 17 April 2026.
  10. ↑ "The Stanford Question Answering Dataset". SQuAD. Retrieved 17 April 2026.
  11. ↑ "Stanford Question Answering Dataset". Kaggle. Retrieved 17 April 2026.
  12. ↑ Moore, Samuel K. (1 January 2020). "Cerebras's Giant Chip Will Smash Deep Learning's Speed Barrier". IEEE Spectrum. Retrieved 17 April 2026.
  13. ↑ Li, Ming; Bi, Ziqian; Wang, Tianyang; Wen, Yizhu; Niu, Qian; Song, Xinyuan; Jiang, Zekun; Liu, Junyu; Peng, Benji; Zhang, Sen; Pan, Xuanhe; Xu, Jiawei; Wang, Jinlang; Chen, Keyu; Caitlyn Heqi Yin; Feng, Pohsun; Liu, Ming (2024-10-08). "Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing". arXiv:2410.05686 [cs.DC].
  14. ↑ "Accelerated PyTorch training on Mac". Apple Developer. Apple. Retrieved 17 April 2026.
  15. ↑ "GitHub - tsawler/go-metal: A high-performance deep learning library for Go that leverages Apple's Metal for GPU acceleration on Apple Silicon". GitHub. Retrieved 17 April 2026.
  16. ↑ "Accelerating the Machine Learning Lifecycle with MLflow". GitHub.
  17. ↑ Quesada, Alberto (28 October 2019). "5 algorithms to train a neural network". Neural Designer Blog. Artelnics. Retrieved 20 April 2026.
  18. ↑ Janishar Ali. "MIT Deep Learning Book (beautiful and flawless PDF version)". GitHub. Retrieved 17 April 2026.
  19. ↑ Nielsen, Michael (2015). "Neural Networks and Deep Learning". Neural Networks and Deep Learning. Determination Press. Retrieved 17 April 2026.