Outline of deep learning
From Wikipedia, the free encyclopedia
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.[1][2][3]
- A field of study
- A branch of artificial intelligence
- A subfield of machine learning
- A subfield of computer science
- A form of representation learning
- A class of methods based on artificial neural networks
- An approach used in computational statistics
History
Precursors
Milestones
- LeNet
- Long short-term memory
- Deep belief network
- AlexNet
- Sequence to sequence learning
- Generative adversarial network
- Residual neural network
- Transformer
- BERT
- Generative pre-trained transformer
- Diffusion model
Related histories
Core concepts
Learning settings
Common tasks
Architectures
Feedforward and convolutional architectures
- Feedforward neural network
- Multilayer perceptron
- Convolutional neural network
- Radial basis function network
- Residual neural network
- U-Net
Recurrent and sequence architectures
- Recurrent neural network
- Long short-term memory
- Gated recurrent unit
- Sequence to sequence learning
- Recursive neural network
Representation-learning architectures
- Autoencoder
- Denoising autoencoder
- Sparse autoencoder
- Variational autoencoder
- Restricted Boltzmann machine
- Deep belief network
Attention and transformer architectures
Generative and probabilistic architectures
- Autoregressive model
- Diffusion model
- Energy-based model
- Generative adversarial network
- Mixture of experts
Graph and memory architectures
Neural network components and techniques
Training and optimization
Datasets and benchmarks
Applications
Computer vision
- Computer vision
- Facial recognition system
- Image classification
- Image segmentation
- Medical imaging
- Object detection
- Optical character recognition
Natural language processing
- Automatic summarization
- Chatbot
- Information retrieval
- Large language model
- Natural language processing
- Neural machine translation
- Question answering
- Sentiment analysis
Speech and audio
Science and medicine
Robotics and control
Recommendation, search, and forecasting
Generative artificial intelligence
- Deepfake
- Generative artificial intelligence
- Large language model
- Speech synthesis
- Text-to-image model
Computer graphics and video games
- Deep Learning Anti-Aliasing (DLAA)
- Deep Learning Super Sampling (DLSS)
Hardware
- AMD Instinct
- AMD XDNA
- Application-specific integrated circuit
- Deep learning processor, Neural processing unit (NPU), or Neural Engine
- Field-programmable gate array
- General-purpose computing on graphics processing units (GPGPU)
- Graphics processing unit
- NVIDIA Deep Learning Accelerator (NVDLA)
- Tensor processing unit
- Vision processing unit
- Wafer-scale integration[12]
Supporting software platforms
Software
Open-source frameworks and libraries
Neural network software
Platforms, tools, and deployment
Algorithms for deep learning and neural networks
Methods and related topics
Representation and metric learning
Generative modeling
- Autoregressive model
- Diffusion model
- Generative adversarial network
- Generative model
- Variational inference
Efficient and scalable deep learning
Reliability, safety, and interpretability
Conferences and workshops
- Annual Meeting of the Association for Computational Linguistics
- Conference on Computer Vision and Pattern Recognition
- Conference on Neural Information Processing Systems
- International Conference on Computer Vision
- International Conference on Learning Representations
- International Conference on Machine Learning
Organizations
Research laboratories and institutions
- Allen Institute for AI
- Alberta Machine Intelligence Institute
- European Laboratory for Learning and Intelligent Systems
- Google DeepMind
- Meta AI
- Mila
- Microsoft Research
- Vector Institute
Companies
Publications
Books
- Deep Learning[18] – Ian Goodfellow and Yoshua Bengio
- Neural Networks and Deep Learning[19] – Michael Nielsen
- Perceptrons – Marvin Minsky and Seymour Papert
