For the TV series episode, see Deep Learning (South Park).
Representing images on multiple layers of abstraction in deep learning[1]
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Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.[2]
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, where they have produced results comparable to and in some cases surpassing human expert performance.[3][4][5]
Early forms of neural networks were inspired by information processing and distributed communication nodes in biological systems, in particular the human brain. However, current neural networks do not intend to model the brain function of organisms, and are generally seen as low quality models for that purpose.[6]
^Schulz, Hannes; Behnke, Sven (1 November 2012). "Deep Learning". KI - Künstliche Intelligenz. 26 (4): 357–363. doi:10.1007/s13218-012-0198-z. ISSN 1610-1987. S2CID 220523562.
^Ciresan, D.; Meier, U.; Schmidhuber, J. (2012). "Multi-column deep neural networks for image classification". 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 3642–3649. arXiv:1202.2745. doi:10.1109/cvpr.2012.6248110. ISBN 978-1-4673-1228-8. S2CID 2161592.
^Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey (2012). "ImageNet Classification with Deep Convolutional Neural Networks" (PDF). NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada. Archived (PDF) from the original on 2017-01-10. Retrieved 2017-05-24.
^"Google's AlphaGo AI wins three-match series against the world's best Go player". TechCrunch. 25 May 2017. Archived from the original on 17 June 2018. Retrieved 17 June 2018.
^"Study urges caution when comparing neural networks to the brain". MIT News | Massachusetts Institute of Technology. 2022-11-02. Retrieved 2023-12-06.
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In U.S. education, deeperlearning is a set of student educational outcomes including acquisition of robust core academic content, higher-order thinking...
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Multimodal learning, in the context of machine learning, is a type of deeplearning using a combination of various modalities of data, such as text, audio...
predictions. A deep Q-network (DQN) is a type of deeplearning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike...
Topological deeplearning (TDL) represents a field at the intersection of topology and deeplearning, offering approaches to analyze and learn from data...
as AI winter. Funding and interest vastly increased after 2012 when deeplearning surpassed all previous AI techniques, and after 2017 with the transformer...
Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...
in 1986. In 1993, a neural history compressor system solved a "Very DeepLearning" task that required more than 1000 subsequent layers in an RNN unfolded...
Deeplearning speech synthesis refers to the application of deeplearning models to generate natural-sounding human speech from written text (text-to-speech)...
chess) after a few days of play against itself using reinforcement learning. In 2020, DeepMind made significant advances in the problem of protein folding...
artificial intelligence and deeplearning; including self-driving cars, healthcare, high-performance computing, and Nvidia DeepLearning Institute (DLI) training...
for training multilayer perceptrons (MLPs) by deeplearning were already known. The first deeplearning MLP was published by Alexey Grigorevich Ivakhnenko...
often built in with deeplearning libraries such as Keras. Time-based learning schedules alter the learning rate depending on the learning rate of the previous...
deeplearning software. On August 9, 2016, it was acquired by Intel, for an estimated $408 million. The company's (now discontinued) open source deep...
Things, and pharmaceuticals. Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets...
with learning connections, was introduced already by Frank Rosenblatt in his book Perceptron. This extreme learning machine was not yet a deeplearning network...
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