Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression...

In U.S. education, deeper learning is a set of student educational outcomes including acquisition of robust core academic content, higher-order thinking...

General Deep Neural Networks". arXiv:1906.09235 [cs.LG]. Xu ZJ, Zhou H (18 May 2021). "Deep Frequency Principle Towards Understanding Why Deeper Learning is...

The transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which...

Deep Learning Super Sampling (DLSS) is a suite of real-time deep learning image enhancement and upscaling technologies developed by Nvidia that are available...

A neural processing unit (NPU), also known as AI accelerator or deep learning processor, is a class of specialized hardware accelerator or computer system...

Deep reinforcement learning (DRL) is a subfield of machine learning that combines principles of reinforcement learning (RL) and deep learning. It involves...

explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical...

compare notable software frameworks, libraries, and computer programs for deep learning applications. Licenses here are a summary, and are not taken to be complete...

Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University...

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...

Topological deep learning (TDL) is a research field that extends deep learning to handle complex, non-Euclidean data structures. Traditional deep learning models...

In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data...

Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring...

"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...

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...

to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field...

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...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

GoogLeNet are CNNs. They also called it "Inception" after a "we need to go deeper" internet meme, a phrase from Inception (2010) the film. Because later,...

Zhejiang University. The company began stock trading using a GPU-dependent deep learning model on 21 October 2016; before then, it had used CPU-based linear...

In deep learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear...

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 speech synthesis refers to the application of deep learning models to generate natural-sounding human speech from written text (text-to-speech)...

sets, and the application of solid mathematical methods. Soon after, deep learning proved to be a breakthrough technology, eclipsing all other methods...

launched the ongoing AI spring, and further increasing interest in deep learning. The transformer architecture was first described in 2017 as a method...

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

chess) after a few days of play against itself using reinforcement learning. In 2020, DeepMind made significant advances in the problem of protein folding...

Neural Networks Through Deep Visualization. Deep Learning Workshop, International Conference on Machine Learning (ICML) Deep Learning Workshop. arXiv:1506...

learning and problem-based learning, is used to teach 21st century skills such as communication and collaboration and foster deeper learning. Deeper learning...