In machine learning, deep learning focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation...

In deep learning, transformer is an architecture based on the multi-head attention mechanism, in which text is converted to numerical representations...

learning algorithm for hidden units, i.e., deep learning. Fundamental research was conducted on ANNs in the 1960s and 1970s. The first working deep learning...

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

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

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

Q-learning algorithm. In 2014, Google DeepMind patented an application of Q-learning to deep learning, titled "deep reinforcement learning" or "deep Q-learning"...

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...

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

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

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

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...

(2021). NYU Deep Learning course, Spring 2020. Event occurs at 05:30. Retrieved 2021-12-22. Alfredo Canziani & Yann Lecun (2021). NYU Deep Learning course...

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

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...

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

processing units started being used to accelerate neural networks and deep learning outperformed previous AI techniques. This growth accelerated further...

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...

Deepfakes (a portmanteau of 'deep learning' and 'fake') are images, videos, or audio that have been edited or generated using artificial intelligence...

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...

chess) after a few days of play against itself using reinforcement learning. DeepMind has since trained models for game-playing (MuZero, AlphaStar), for...

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

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

Hebbian learning in these networks,: Chapter 19, 21  and noted that a fully cross-coupled perceptron network is equivalent to an infinitely deep feedforward...

artificial intelligence and deep learning; including self-driving cars, healthcare, high-performance computing, and Nvidia Deep Learning Institute (DLI) training...

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

"Provable Bounds for Learning Some Deep Representations". Proceedings of the 31st International Conference on Machine Learning. PMLR: 584–592. Szegedy...

class of supervised neural network models). In recent developments of deep learning the rectified linear unit (ReLU) is more frequently used as one of the...