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Deep-learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, convolutional neural networks and transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs ...
Geoffrey Everest Hinton CC FRS FRSC [12] (born 6 December 1947) is a British-Canadian computer scientist and cognitive psychologist, most noted for his work on artificial neural networks.
Deep 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 manual feature engineering than prior ...
Comparison of deep learning software. The following table compares notable software frameworks, libraries and computer programs for deep learning .
Neural network (machine learning) An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Part of a series on.
“We’re applying deep AI learning models, and we've literally trained that model with millions and billions of transactions,” says Anthony Cahill, global head of value-added services at Visa.
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