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F
faculty.sites.iastate.edu
research
https://faculty.sites.iastate.edu/tesfatsi/archive/tesfatsi/DeepLearningInNeu…
Neural Networks 61 (2015) 85–117 Contents lists available at ScienceDirect Neural Networks journal homepage: www.elsevier.com/locate/neunet Review Deep learning in neural networks: An overview Jürgen Schmidhuber The Swiss AI Lab IDSIA, Istituto Dalle Molle di Studi sull’Intelligenza Artificiale, University of Lugano & SUPSI, Galleria 2, 6928 Manno-Lugano, Switzerland a r t i c l e i n f o Article history: Received 2 May 2014 Received in revised form 12 September 2014 Accepted 14 September 2014 Available online 13 October 2014 Keywords: Deep learning Supervised learning Unsupervised learning Reinforcement learning Evolutionary computation a b s t r a c t In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. Introduction to Deep Learning (DL) in Neural Networks (NNs).................................................................................................................................. The present survey, however, will focus on the narrower, but now commercially important, subfield of Deep Learning (DL) in Artificial Neural Networks (NNs).
M
mdpi.com
article
https://www.mdpi.com/2073-8994/15/9/1723
Deep learning techniques have found applications across diverse fields, enhancing the efficiency and effectiveness of decision-making processes.
T
techtarget.com
article
https://www.techtarget.com/searchenterpriseai/feature/Deep-learnings-role-in-…
5 ways deep learning is changing the field of machine learning · Framing a problem · Automation of human insight · More efficient use of data.
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news.mit.edu
research
https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department. Neural nets were a major area of research in both neuroscience and computer science until 1969, when, according to computer science lore, they were killed off by the MIT mathematicians Marvin Minsky and Seymour Papert, who a year later would become co-directors of the new MIT Artificial Intelligence Laboratory. By the 1980s, however, researchers had developed algorithms for modifying neural nets’ weights and thresholds that were efficient enough for networks with more than one layer, removing many of the limitations identified by Minsky and Papert.
M
medium.com
article
https://medium.com/@shubavarma/evolution-of-deep-learning-and-artificial-neur…
While ML focuses on algorithms that learn from data and make predictions, DL employs neural networks with multiple layers to automatically learn
O
online.nyit.edu
research
https://online.nyit.edu/blog/deep-learning-and-neural-networks
[Skip to main content](https://online.nyit.edu/blog/deep-learning-and-neural-networks#main-content). * [Curriculum](https://online.nyit.edu/ms-data-science/curriculum). * [Careers](https://online.nyit.edu/ms-data-science/careers). * [Study at New York Tech](https://online.nyit.edu/ms-data-science/new-york). * [Apply Now](https://online.nyit.edu/blog/deep-learning-and-neural-networks#apply-now). [](https://online.nyit.edu/). * [Curriculum](https://online.nyit.edu/ms-data-science/curriculum). * [Careers](https://online.nyit.edu/ms-data-science/careers). * [Study at New York Tech](https://online.nyit.edu/ms-data-science/new-york). * [Apply Now](https://online.nyit.edu/blog/deep-learning-and-neural-networks#apply-now). [Home](https://online.nyit.edu/)[Online Degrees Blog at New York Tech](https://online.nyit.edu/blog)Deep Learning and Neural Networks: The Future of Machine Learning. . In contrast, deep learning programs use thousands of layers to train a model.2. An [Online Master’s in Data Science](https://online.nyit.edu/ms-data-science) from the New York Institute of Technology can equip you with the knowledge and skills you need to thrive in high-demand, data-driven careers. 2. Retrieved on May 9, 2025, from [ibm.com/think/topics/deep-learning](https://www.ibm.com/think/topics/deep-learning). 8. Retrieved on May 9, 2025, from [neurond.com/blog/10-applications-of-deep-learning-in-artificial-intelligence](https://www.neurond.com/blog/10-applications-of-deep-learning-in-artificial-intelligence). New York Institute of Technology has engaged [Everspring](https://online.nyit.edu/privacy-policy), a leading provider of education and technology services, to support select aspects of program delivery. [](https://online.nyit.edu/).
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ncbi.nlm.nih.gov
official
https://www.ncbi.nlm.nih.gov/books/NBK597497/
The analysis is structured around the main components of deep learning architectures, focusing on convolutional neural networks and autoencoders. In this definition, the term parametric holds due to the parameters that we need to learn during the training of these models, the non-linearity due to the non-linear functions that they are composed of, and the hierarchical representation due to the fact that the output of one function is used as the input of the next in a hierarchical way. [53], sometimes called “LeNet.” Such architecture is typically composed of two parts: the first one is based on convolution operations and learns the features for the image and the second part flattens the features and inputs them to a set of fully connected layers (in other words, a multilayer perceptron) for performing the classification/regression (*see* illustration in Fig. 18). In this chapter, we presented the basic principles of deep learning, covering both perceptrons and convolutional neural networks.
E
en.wikipedia.org
article
https://en.wikipedia.org/wiki/Deep_learning
[Jump to content](https://en.wikipedia.org/wiki/Deep_learning#bodyContent). * [(Top)](https://en.wikipedia.org/wiki/Deep_learning#). * [1 Overview](https://en.wikipedia.org/wiki/Deep_learning#Overview). * [2 Interpretations](https://en.wikipedia.org/wiki/Deep_learning#Interpretations). * [3 History](https://en.wikipedia.org/wiki/Deep_learning#History)Toggle History subsection. * [3.1 Before 1980](https://en.wikipedia.org/wiki/Deep_learning#Before_1980). * [3.2 1980s-2000s](https://en.wikipedia.org/wiki/Deep_learning#1980s-2000s). * [3.3 2000s](https://en.wikipedia.org/wiki/Deep_learning#2000s). * [4.1 Deep neural networks](https://en.wikipedia.org/wiki/Deep_learning#Deep_neural_networks). * [4.1.1 Challenges](https://en.wikipedia.org/wiki/Deep_learning#Challenges). * [5 Hardware](https://en.wikipedia.org/wiki/Deep_learning#Hardware). * [6.2 Image recognition](https://en.wikipedia.org/wiki/Deep_learning#Image_recognition). * [6.7 Bioinformatics](https://en.wikipedia.org/wiki/Deep_learning#Bioinformatics). * [6.11 Image restoration](https://en.wikipedia.org/wiki/Deep_learning#Image_restoration). * [6.13 Materials science](https://en.wikipedia.org/wiki/Deep_learning#Materials_science). * [6.14 Military](https://en.wikipedia.org/wiki/Deep_learning#Military). * [6.17 Image reconstruction](https://en.wikipedia.org/wiki/Deep_learning#Image_reconstruction). * [9.1 Theory](https://en.wikipedia.org/wiki/Deep_learning#Theory). * [9.2 Errors](https://en.wikipedia.org/wiki/Deep_learning#Errors). * [10 See also](https://en.wikipedia.org/wiki/Deep_learning#See_also). * [11 References](https://en.wikipedia.org/wiki/Deep_learning#References). * [12 Further reading](https://en.wikipedia.org/wiki/Deep_learning#Further_reading). * [Article](https://en.wikipedia.org/wiki/Deep_learning "View the content page [c]"). * [Read](https://en.wikipedia.org/wiki/Deep_learning). * [Read](https://en.wikipedia.org/wiki/Deep_learning). * [Deep learning](https://en.wikipedia.org/wiki/Deep_learning). (p.112 [[81]](https://en.wikipedia.org/wiki/Deep_learning#cite_note-81)). **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-ELMAN_256-0)**Elman, Jeffrey L. **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-BLAKESLEE_259-0)**S. **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-260)**Mazzoni, P.; Andersen, R. **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-261)**O'Reilly, Randall C. **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-262)**Testolin, Alberto; Zorzi, Marco (2016). **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-265)**Cash, S.; Yuste, R. **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-266)**Olshausen, B; Field, D (1 August 2004). **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-METZ2013_270-0)**Metz, C. **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-271)**Gibney, Elizabeth (2016). **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-274)**Metz, Cade (6 November 2017). **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-277)**Marcus, Gary (14 January 2018). **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-Knight_2017_278-0)**Knight, Will (14 March 2017). **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-280)**Alex Hern (18 June 2015). **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-285)**Zhu, S.C.; Mumford, D. **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-286)**Miller, G. **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-287)**Eisner, Jason. **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-291)**Gibney, Elizabeth (2017). **[^](https://en.wikipedia.org/wiki/Deep_learning#cite_ref-292)**Tubaro, Paola (2020). 64 languages[Add topic](https://en.wikipedia.org/wiki/Deep_learning#).