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Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

natural-language-processing deep-neural-networks deep-learning neural-network deep-reinforcement-learning speech-recognition neural-networks deep-learning-algorithms artificial-neural-networks deep-learning-tutorial audio-recognition anns hierarchical-learning biological-brains social-network-filtering board-game-programs

Updated Jul 16, 2020
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Develop-Packt / Discovering-the-Building-Blocks-of-Neural-Networks-with-PyTorch

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Discover the main building blocks of neural networks and understand the three main neural network architectures. Explore the process of solving a regression data problem

deep-learning pytorch recurrent-neural-networks neural-networks artificial-neural-networks convolutional-neural-networks gradient-descent data-preparation loss-functions perceptrons anns

Updated Mar 28, 2020
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