Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of...

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the...

A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention...

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...

"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...

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

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

first deep learning MLP was published by Alexey Grigorevich Ivakhnenko and Valentin Lapa in 1965, as the Group Method of Data Handling. The first deep learning...

An AI accelerator, deep learning processor, or neural processing unit (NPU) is a class of specialized hardware accelerator or computer system designed...

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

Deep learning super sampling (DLSS) is a family of real-time deep learning image enhancement and upscaling technologies developed by Nvidia that are exclusive...

generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms. Machine learning and statistics are...

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

as AI winter. Funding and interest vastly increased after 2012 when deep learning surpassed all previous AI techniques, and after 2017 with the transformer...

Multimodal learning, in the context of machine learning, is a type of deep learning using a combination of various modalities of data, such as text, audio...

predictions. A deep Q-network (DQN) is a type of deep learning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike...

Topological deep learning (TDL) represents a field at the intersection of topology and deep learning, offering approaches to analyze and learn from data...

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

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

in 1986. In 1993, a neural history compressor system solved a "Very Deep Learning" task that required more than 1000 subsequent layers in an RNN unfolded...

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

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

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

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...

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...

Deep learning anti-aliasing (DLAA) is a form of spatial anti-aliasing created by Nvidia. DLAA depends on and requires Tensor Cores available in Nvidia...

Things, and pharmaceuticals. Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets...

for training multilayer perceptrons (MLPs) by deep learning were already known. The first deep learning MLP was published by Alexey Grigorevich Ivakhnenko...

many different fields including healthcare. Attention (machine learning) Deep learning Differentiable neural computer Gated recurrent unit Highway network...

with learning connections, was introduced already by Frank Rosenblatt in his book Perceptron. This extreme learning machine was not yet a deep learning network...