WebDeep learning is a form of machine learning that utilizes a neural network to transform a set of inputs into a set of outputs via an artificial neural network.Deep learning methods, often using supervised learning with labeled datasets, have been shown to solve tasks that involve handling complex, high-dimensional raw input data such as images, with less …

WebDeep learning super sampling (DLSS) is a family of real-time deep learning image enhancement and upscaling technologies developed by Nvidia that are exclusive to its RTX line of graphics cards, and available in a number of video games.The goal of these technologies is to allow the majority of the graphics pipeline to run at a lower resolution …

WebTransfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. This area of research bears some relation to the long history of …

WebIf deep learning is used, the architecture of the neural network must also be chosen by the machine learning expert. Each of these steps may be challenging, resulting in significant hurdles to using machine learning. AutoML aims to simplify these steps for non-experts, and make the practice of machine learning more efficient. ...

WebReinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.. Reinforcement …

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WebThis learning system was a forerunner of the Q-learning algorithm. In 2014, Google DeepMind patented an application of Q-learning to deep learning, titled "deep reinforcement learning" or "deep Q-learning" that can play Atari 2600 games at expert human levels. Variants Deep Q-learning

WebMulti-agent reinforcement learning (MARL) is a sub-field of reinforcement learning.It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are opposed to the interests of other …

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WebVisit the NVIDIA NGC catalog to pull containers and quickly get up and running with deep learning. Single GPU Training Performance of NVIDIA A100, A40, A30, A10, T4 and V100. ... Wikipedia 2020/01/01: A100-SXM4-80GB: 1.13.0a0: BERT Large Pre-Training Phase 1: 853 sequences/sec: 1x A100: DGX A100: 22.07-py3: Mixed: 512: Wikipedia 2020/01/01 ...