ĭeep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, 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, where they have produced results comparable to and in some cases surpassing human expert performance. Methods used can be either supervised, semi-supervised or unsupervised. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Representing images on multiple layers of abstraction in deep learning ĭeep learning is the subset of machine learning methods which are based on artificial neural networks with representation learning.
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