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Branch of machine learning
In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation
Deep_learning
Algorithm for modelling sequential data
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which input data
Transformer_(deep_learning)
Subset of artificial intelligence
correct learning provides a mathematical and statistical framework for describing machine learning. Most traditional machine learning and deep learning algorithms
Machine_learning
Image upscaling technology by Nvidia
Deep Learning Super Sampling (DLSS) is a suite of real-time deep learning image enhancement and upscaling technologies developed by Nvidia that are available
Deep_Learning_Super_Sampling
4th episode of the 26th season of South Park
"Deep Learning" is the fourth episode of the twenty-sixth season of the American animated television series South Park, and the 323rd episode of the series
Deep_Learning_(South_Park)
Machine learning that combines deep learning and reinforcement learning
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the
Deep_reinforcement_learning
Computational model used in machine learning
network is typically called a deep neural network if it has at least two hidden layers. Deep neural networks are capable of learning sophisticated hierarchical
Neural network (machine learning)
Neural_network_(machine_learning)
Deep learning architecture
Mamba is a deep learning architecture focused on sequence modeling. It was developed by two researchers Albert Gu from Carnegie Mellon University and
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
Machine learning technique
In deep learning, fine-tuning is the process of adapting a computational model trained for one task (the upstream task) to perform a different, usually
Fine-tuning_(deep_learning)
Intelligence in machines
units (GPUs) started being used to accelerate neural networks, and deep learning outperformed previous AI techniques. This growth accelerated further
Artificial_intelligence
Machine learning methods using multiple input modalities
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images
Multimodal_learning
Model-free reinforcement learning algorithm
Q-learning algorithm. In 2014, Google DeepMind patented an application of Q-learning to deep learning, entitled "deep reinforcement learning" or "deep Q-learning
Q-learning
and tools used for machine learning, deep learning, natural language processing, computer vision, reinforcement learning, artificial general intelligence
Lists of open-source artificial intelligence software
Lists_of_open-source_artificial_intelligence_software
Type of feedforward neural network
that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different
Convolutional_neural_network
Type of large language model
artificial intelligence chatbots. GPTs are based on the transformer, a deep learning architecture. They are pre-trained on large datasets of unlabeled content
Generative pre-trained transformer
Generative_pre-trained_transformer
Tabular comparison of deep learning software
compare notable software frameworks, libraries, and computer programs for deep learning applications. Licenses here are a summary, and are not taken to be complete
Comparison of deep learning software
Comparison_of_deep_learning_software
In U.S. education, deeper learning is a set of student educational outcomes including acquisition of robust core academic content, higher-order thinking
Deeper_learning
Deep learning model structure
A layer in a deep learning model is a stage that take the previous stage process it and pass to the next stage or network topology in the model's architecture
Layer_(deep_learning)
Type of feedforward neural network
In deep learning, a multilayer perceptron (MLP) is a kind of modern feedforward neural network consisting of fully connected neurons with nonlinear activation
Multilayer_perceptron
A 1971 paper described a deep network with the equivalent of eight layers trained by this method. The first deep learning multilayer perceptron trained
History of artificial neural networks
History_of_artificial_neural_networks
Defunct American cloud computing company
deep learning software. On August 9, 2016, it was acquired by Intel, for an estimated $408 million. The company's (now discontinued) open-source deep
Nervana_Systems
Principle in artificial intelligence
consistently outperformed the hand-crafted approaches of the 1970s, and deep learning has continued this trend. Computer vision. Algorithms that were assumed
Bitter_lesson
Method of speech synthesis that uses deep neural networks
Deep learning speech synthesis refers to the application of deep learning models to generate natural-sounding human speech from written text (text-to-speech)
Deep learning speech synthesis
Deep_learning_speech_synthesis
Field of machine learning
Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. While supervised learning and
Reinforcement_learning
Hardware acceleration unit for artificial intelligence tasks
A neural processing unit (NPU), also known as an AI accelerator or deep learning processor, is a class of specialized hardware accelerator or computer
Neural_processing_unit
and the application of rigorous mathematical methods. Soon after, deep learning proved to be a breakthrough technology, eclipsing all other methods
History of artificial intelligence
History_of_artificial_intelligence
Family of convolutional neural networks
"Provable Bounds for Learning Some Deep Representations". Proceedings of the 31st International Conference on Machine Learning. PMLR: 584–592. arXiv:1310
Inception (deep learning architecture)
Inception_(deep_learning_architecture)
American multinational technology company
artificial intelligence and deep learning; including self-driving cars, healthcare, high-performance computing, and Nvidia Deep Learning Institute (DLI) training
Nvidia
Computer graphics anti-aliasing algorithm
Deep Learning Anti-Aliasing (DLAA) is a form of spatial anti-aliasing developed by Nvidia. DLAA depends on and requires Tensor Cores available in Nvidia
Deep_Learning_Anti-Aliasing
Overview of and topical guide to deep learning
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
Outline_of_deep_learning
Research field in deep learning
Topological deep learning (TDL) is a research field that extends deep learning to handle complex, non-Euclidean data structures. Traditional deep learning models
Topological_deep_learning
Decentralized machine learning
things, and pharmaceuticals. Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets
Federated_learning
AI research laboratory
chess) after a few days of play against itself using reinforcement learning. DeepMind has since trained models for game-playing (MuZero, AlphaStar), for
Google_DeepMind
Academic conference in machine learning
Experiment: Deep Learning Pioneers Take on Scientific Publishing". KDnuggets. Retrieved 13 September 2025. "13th Annual International Conference on Learning Representations
International Conference on Learning Representations
International_Conference_on_Learning_Representations
Subfield of artificial intelligence
state-of-the-art machine learning for solving a wider range of problems more effectively. Neuro-symbolic AI recognises the value of deep learning as the “substrate”
Neuro-symbolic_AI
Nonprofit deep learning and AI research group
on deep learning and artificial intelligence. It was founded in 2016 by Jeremy Howard and Rachel Thomas with the goal of democratizing deep learning. They
Fast.ai
American computer scientist, entrepreneur (born 1976)
known for his contributions to the field of artificial intelligence and deep learning, particularly in the development of transformer models and natural language
Noam_Shazeer
AI whose outputs can be understood by humans
overlapping with interpretable AI, interpretable machine learning and explainable machine learning (XML), is a field of research that explores methods that
Explainable artificial intelligence
Explainable_artificial_intelligence
Machine learning technique
queries. This is dot-product attention without softmax. During the deep learning era, attention mechanism was developed to solve similar problems in
Attention_(machine_learning)
types of computer-aided chemistry would benefit from machine learning. A deep learning AI-based process has been developed that uses genome databases
Applications of artificial intelligence
Applications_of_artificial_intelligence
French artificial intelligence company
learning methodologies and the development of predictive models for different disease areas, mainly oncology. Courtiol, Pierre et al. “Deep learning-based
Owkin
Canadian computer scientist
scientist most noted for his work on artificial neural networks and deep learning. In 2012, Krizhevsky, Ilya Sutskever and their PhD advisor Geoffrey
Alex_Krizhevsky
Deep learning artificial intelligence research team
Google Brain was a deep learning artificial intelligence research team that served as the sole AI branch of Google before being incorporated under the
Google_Brain
American semiconductor company
supercomputers, and related software to power artificial intelligence deep-learning applications such as inference engines. Products include its wafer scale
Cerebras_Systems
Class of artificial neural network
studies for Hebbian learning in these networks, and noted that a fully cross-coupled perceptron network is equivalent to an infinitely deep feedforward network
Recurrent_neural_network
Chinese artificial intelligence company
Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., doing business as DeepSeek, is a Chinese artificial intelligence (AI) company
DeepSeek
Type of artificial neural network
these early efforts did not lead to a working learning algorithm for hidden units, i.e., deep learning. In 1965, Alexey Grigorevich Ivakhnenko and Valentin
Feedforward_neural_network
Tuning parameter (hyperparameter) in optimization
often built in with deep learning libraries such as Keras. Time-based learning schedules alter the learning rate depending on the learning rate of the previous
Learning_rate
Realistic artificially generated media
Deepfakes (a portmanteau of 'deep learning' and 'fake') are images, videos, or audio that have been edited or generated using artificial intelligence
Deepfake
Type of artificial neural network
In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple
Deep_belief_network
Machine learning technique
Survey on Transfer Learning". arXiv:1911.02685 [cs.LG]. NIPS 2016 tutorial: "Nuts and bolts of building AI applications using Deep Learning" by Andrew Ng,
Transfer_learning
Machine learning paradigm
Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals
Self-supervised_learning
Image-generation models developed by OpenAI
developed by OpenAI. A text-to-image variant of the GPT family, it uses deep learning methodologies to generate digital images from natural language descriptions
GPT_Image
Methods in artificial intelligence research
increase the power of neural networks." Over the next several years, deep learning had spectacular success in handling vision, speech recognition, speech
Symbolic artificial intelligence
Symbolic_artificial_intelligence
Structuring text as input to generative artificial intelligence
in-context learning is temporary. Training models to perform in-context learning can be viewed as a form of meta-learning, or "learning to learn". Research
Prompt_engineering
Machine learning conference
The Deep Learning Indaba is an annual conference and educational event that aims to strengthen machine learning and artificial intelligence (AI) capacity
Deep_Learning_Indaba
Algorithm in computer image processing
especially Deep Learning algorithms, but evolutionary algorithms such as particle swarm optimization can also be useful to perform this task. Deep learning has
Landmark_detection
Parameter-efficient fine-tuning technique for large language models
350 gigabytes). The technique applies broadly to any dense layers in deep learning models, though it has been most extensively studied in the context of
LoRA_(machine_learning)
Set of learning techniques in machine learning
In machine learning (ML), representation learning or feature learning is a set of techniques that allow a system to automatically discover the representations
Representation_learning
2017 paper conceptualizing the transformer-based deep learning architecture
research paper on machine learning authored by eight scientists and engineers working at Google. The paper introduced a new deep learning architecture known
Attention_Is_All_You_Need
Machine learning technique
previous section described MoE as it was used before the era of deep learning. After deep learning, MoE found applications in running the largest models, as
Mixture_of_experts
Process of automating the application of machine learning
of their model. If deep learning is used, the architecture of the neural network must also be chosen manually by the machine learning expert. Each of these
Automated_machine_learning
control, procedural content generation (PCG) and deep learning-based content generation. Machine learning is a subset of artificial intelligence that uses
Machine learning in video games
Machine_learning_in_video_games
American company
is Ceva's family of low-power artificial intelligence processors for deep learning. NeuPro processors are self-contained, specialized AI processors, scaling
Ceva_(semiconductor_company)
Polish-American computer scientist
University of Warsaw. He then began his PhD at New York University (NYU) in deep learning under the supervision of Yann LeCun and Rob Fergus. Zaremba graduated
Wojciech_Zaremba
Store that does not contain a checkout
products. In the check-out phase, stores utilize sensor fusion and deep learning for computer vision to allow customers to walk out with their products
Cashierless_store
Measure of required computing power
CSET has reported on the various bottlenecks which could explain why deep learning needs for compute have slow down: training is expensive and training
Compute_(machine_learning)
Software company
specializes in developing natural-sounding speech synthesis software using deep learning. It was founded in 2022 by Polish entrepreneurs Piotr Dąbkowski and
ElevenLabs
open-source deep learning framework for deep neural networks. Apache Singa — library for deep learning. Apache SystemDS — machine learning system for the
List_of_Python_software
Real-time text-to-speech AI tool
a significant transformation with the introduction of deep learning approaches. In 2016, DeepMind's publication of the WaveNet paper marked a shift toward
15.ai
Canadian AI researcher
working in the field of artificial intelligence. He specializes in deep learning, probabilistic graphical models, and large-scale optimization. Salakhutdinov's
Ruslan_Salakhutdinov
Overview of and topical guide to machine learning
Comparison of machine learning software Comparison of deep learning software Amazon Machine Learning Microsoft Azure Machine Learning Studio DistBelief (replaced
Outline_of_machine_learning
Image dataset
processing units (GPUs) during training, an essential ingredient of the deep learning revolution. According to The Economist, "Suddenly people started to
ImageNet
Engineering applied to artificial intelligence
for example) to determine the most suitable machine learning algorithm, including deep learning paradigms. Once an algorithm is chosen, optimizing it
Artificial intelligence engineering
Artificial_intelligence_engineering
American computer scientist (born 1979)
Washington University in St. Louis. He is known for his contributions to deep learning systems. Chen is an IEEE Fellow and an AAAI Fellow. Chen completed his
Yixin_Chen
American artificial intelligence researcher
education, cofounding Coursera and DeepLearning.AI. He has spearheaded many efforts to "democratize deep learning" teaching over 8 million students through
Andrew_Ng
Machine-learning and computational-neuroscience conference
machine learning and although the 'Neural' in the NeurIPS acronym had become something of a historical relic, the resurgence of deep learning in neural
Conference on Neural Information Processing Systems
Conference_on_Neural_Information_Processing_Systems
Line of Nvidia produced servers and workstations
Nvidia DGX (Deep GPU Xceleration) is a series of servers and workstations designed by Nvidia, primarily geared towards enhancing deep learning applications
Nvidia_DGX
British-Canadian computer scientist (born 1947)
to propose the approach. Hinton is viewed as a leading figure in the deep learning community. The image-recognition neural network AlexNet, designed in
Geoffrey_Hinton
Deep learning software
machine learning library, a scientific computing framework, and a scripting language based on Lua. It provides LuaJIT interfaces to deep learning algorithms
Torch_(machine_learning)
Cloud-based Jupyter Notebook environment
and TPUs, making it popular among researchers and students working on deep learning and data science projects. Supports Python 3, R, and Julia Built on
Google_Colab
Vladimir (1995). "Support-vector networks". Machine Learning. 20 (3): 273–297. doi:10.1007/BF00994018. "Deep Blue". IBM. 10 November 2025. Hochreiter, Sepp;
Timeline_of_machine_learning
Machine learning model for speech
non-English languages into English. Whisper is a weakly-supervised deep learning acoustic model, made using an encoder-decoder transformer architecture
Whisper (speech recognition system)
Whisper_(speech_recognition_system)
Processing of natural language by a computer
Word2vec. In the 2010s, representation learning and deep neural network-style (featuring many hidden layers) machine learning methods became widespread in natural
Natural_language_processing
Software tool
Deep Learning Studio is a software tool that aims to simplify the creation of deep learning models used in artificial intelligence. It is compatible with
Deep_Learning_Studio
Type of database that uses vectors to represent other data
from the raw data using machine learning methods such as feature extraction algorithms, word embeddings or deep learning networks. The goal is that semantically
Vector_database
Concept of open-source software applied to Global connections
Neighbors (kNN), Naive Bayes and Support Vector Machines (SVM). Open-source deep learning framework as Torch was released in 2002 and made open-source with Torch7
Open-source artificial intelligence
Open-source_artificial_intelligence
American psychologist (1928–1971)
field of artificial intelligence. He is sometimes called the father of deep learning for his pioneering work on artificial neural networks. Rosenblatt was
Frank_Rosenblatt
Technology and methods used to provide imaging-based automatic inspection and analysis
are generally done by a CPU, a GPU, a FPGA or a combination of these. Deep learning training and inference impose higher processing performance requirements
Machine_vision
Software program
Neural Networks Through Deep Visualization. Deep Learning Workshop, International Conference on Machine Learning (ICML) Deep Learning Workshop. arXiv:1506
DeepDream
In December 2021, researchers at Google DeepMind collaborated with mathematicians to apply machine learning to problems in knot theory and representation
List of mathematical discoveries made with artificial intelligence
List_of_mathematical_discoveries_made_with_artificial_intelligence
Paradigm in machine learning that uses no classification labels
(PCA), Boltzmann machine learning, and autoencoders. After the rise of deep learning, most large-scale unsupervised learning has been done by training
Unsupervised_learning
Semi-annual technology conference held by Nvidia
intelligence and deep learning, including: self-driving cars, healthcare, high performance computing, professional visualization, and Nvidia Deep Learning Institute
Nvidia_GTC
Several deep learning and artificial neural network models have shown accuracy similar to that of human pathologists, and a study of deep learning assistance
Artificial intelligence in healthcare
Artificial_intelligence_in_healthcare
Usage of artificial intelligence to generate music
intelligence had been made, with generative adversarial networks (GANs) and deep learning being used to help AI compose more original music that is more complex
Artificial intelligence in music
Artificial_intelligence_in_music
Computer scientist (born 1986)
specializes in machine learning. He has made several major contributions to the field of deep learning, including sequence-to-sequence learning, reasoning models
Ilya_Sutskever
French software engineer, artificial intelligence (born 1989)
creator of the Keras deep-learning library released in 2015. His research focuses on computer vision, the application of machine learning to formal reasoning
François_Chollet
Type of activation function
model Layer (deep learning) Brownlee, Jason (8 January 2019). "A Gentle Introduction to the Rectified Linear Unit (ReLU)". Machine Learning Mastery. Retrieved
Rectified_linear_unit
Mathematical procedure
content and collaborative data In recent years a number of neural and deep-learning techniques have been proposed, some of which generalize traditional
Matrix factorization (recommender systems)
Matrix_factorization_(recommender_systems)
British autonomous vehicle technology company
focused on developing self-driving vehicle systems through end-to-end deep learning. Founded in 2017 by researchers from the University of Cambridge, Wayve’s
Wayve
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