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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)
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
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
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
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)
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 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
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
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
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
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
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
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
Deep learning model structure
A layer in a deep learning model is a structure or network topology in the model's architecture, which takes information from the previous layers and
Layer_(deep_learning)
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
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
used to accelerate machine learning and deep learning workloads Horovod — distributed training framework for deep learning Hugging Face Transformers —
Comparison of machine learning software
Comparison_of_machine_learning_software
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
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
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
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
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
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
Machine learning technique
the previous state. Additional surveys of the attention mechanism in deep learning are provided by Niu et al. and Soydaner. The major breakthrough came
Attention_(machine_learning)
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
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
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
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
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
Type of large language model
in generative artificial intelligence chatbots. GPTs are based on a deep learning architecture called the transformer. They are pre-trained on large datasets
Generative pre-trained transformer
Generative_pre-trained_transformer
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
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
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
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)
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
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
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
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
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
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
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
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
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
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
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 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
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
Academic conference in machine learning
International Conference on Machine Learning (ICML) is an international academic conference in machine learning held annually since 1980. It is the oldest
International Conference on Machine Learning
International_Conference_on_Machine_Learning
Study of viral material
that harnesses machine learning have emerged to improve the deficiencies of reference database similarity approaches. Deep learning has demonstrated advantages
Virome_analysis
Type of artificial neural network
class of supervised neural network models). In recent developments of deep learning, the rectified linear unit (ReLU) is more frequently used as one of
Feedforward_neural_network
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
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 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
Concept of open-source software applied to AI
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
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
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
Software program
Neural Networks Through Deep Visualization. Deep Learning Workshop, International Conference on Machine Learning (ICML) Deep Learning Workshop. arXiv:1506
DeepDream
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
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)
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
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
Machine learning technique
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
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
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
Machine learning technique
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related
Transfer_learning
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)
Image dataset
processing units (GPUs) during training, an essential ingredient of the deep learning revolution. According to The Economist, "Suddenly people started to
ImageNet
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
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)
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
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
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
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
2017 research paper by Google
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 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)
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
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
Canadian computer scientist (born 1964)
computer scientist, and a pioneer of artificial neural networks and deep learning. Bengio received the 2018 ACM A.M. Turing Award, often referred to as
Yoshua_Bengio
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
Soviet–Ukrainian mathematician and computer scientist
(GMDH), a method of inductive statistical learning, for which he is considered as one of the founders of deep learning. Aleksey was born in Kobelyaky, Poltava
Alexey_Ivakhnenko
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
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
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
German computer scientist (born 1963)
field of deep learning, in favour of Geoffrey Hinton, Yoshua Bengio and Yann LeCun, who shared the 2018 Turing Award for their work in deep learning. He wrote
Jürgen_Schmidhuber
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
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)
Erroneous AI-generated content presented as true
various ways); changes in the training process, such as using reinforcement learning; and post-processing methods that can correct hallucinations in the output
Hallucination (artificial intelligence)
Hallucination_(artificial_intelligence)
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
Branch of computer science
circumstances or configurations. Deep learning is employed for analysis of high-dimensional data. For instance, deep learning techniques can aid in examining
AI-assisted reverse engineering
AI-assisted_reverse_engineering
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
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
Chinese-American computer scientist
researcher, and engineer, known for his research in speech recognition, deep learning, and multi-modal artificial intelligence. He is a Fellow of the Association
Dong_Yu_(computer_scientist)
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
Machine learning model for vision processing
exaFLOPs. Transformer (machine learning model) Convolutional neural network Attention (machine learning) Perceiver Deep learning PyTorch TensorFlow All positional
Vision_transformer
Recognition and Machine Learning Ian Goodfellow and Yoshua Bengio – Deep Learning John Paul Mueller and Luca Massaron - Machine Learning For Dummies Marvin
List_of_computer_books
Estimate object properties from a finite number of projections
reconstruction algorithms. Except for precision learning, using conventional reconstruction methods with deep learning reconstruction prior is also an alternative
Tomographic_reconstruction
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