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youtube.com
video
https://www.youtube.com/watch?v=AA2ettRM6_Q
Neural Networks Explained: From 1943 Origins to Deep Learning Revolution 🚀 | AI History & Evolution
The AI Guy
1400 subscribers
258 likes
10587 views
10 Jun 2024
Discover the fascinating history of neural networks, from their origins in 1943 to the groundbreaking deep learning advancements of today. Learn how pioneering scientists like Warren McCulloch, Walter Pitts, Frank Rosenblatt, John Hopfield, Geoffrey Hinton, and others contributed to this revolutionary field. Understand key developments like the perceptron, backpropagation, and the role of GPUs in transforming AI. Join us on this journey through time to see how neural networks have evolved to shape modern machine learning and artificial intelligence. 🚀 #NeuralNetworks #DeepLearning #AIHistory #MachineLearning #ArtificialIntelligence
9 comments
G
galileo-unbound.blog
article
https://galileo-unbound.blog/2025/02/05/a-short-history-of-neural-networks/
* ai, Artificial Intelligence, Attention mechanism, convolutional neural network, Deep Learning, History of Physics, Hopfield network, Machine Learning, neural networks, Neurodynamics, Nonlinear Dynamics, recurrent neural network, technology, van der Pol oscillator. Drawing from the work of McCulloch and Pitts, his team constructed a software system and then constructed a hardware model that adaptively updated the strength of the inputs, that they called neural weights, as it was trained on test images. PDP was an exciting framework for artificial intelligence, and it captured the general behavior of natural neural networks, but it had a serious problem: How could all of the neural weights be trained? The breakthrough that propelled Geoff Hinton to world-wide acclaim was the success of AlexNet, a neural network constructed by his graduate student Alex Krizhevsky at Toronto in 2012 consisting of 650,000 neurons with 60 million parameters that were trained using two early Nvidia GPUs. It won the ImageNet challenge that year, enabled by its deep architecture and representing a marked advancement that has been proceeding unabated today.
R
researchgate.net
research
https://www.researchgate.net/publication/374723059_Artificial_Neural_Networks…
This chapter contains a description of the historical evolution of artificial neural networks since their inception.
D
dataversity.net
article
https://www.dataversity.net/articles/a-brief-history-of-neural-networks/
Deep learning uses neural networks, a data structure design loosely inspired by the layout of biological neurons. (It should be noted, Rosenblatt’s primary goal was not to build a computer that could recognize and classify images, but to gain insights about how the human brain worked.) The Perceptron neural network was originally programmed with two layers, the input layer and the output layer. This was the first design of a deep learning model using a convolutional neural network. The early designs of neural networks (such as the Perceptron) did not include hidden layers, but two obvious ones (input/output). In 1989, deep learning became an actuality when Yann LeCun, et al., experimented with the standard backpropagation algorithm (created in 1970), applying it to a neural network. In 2009, Nvidia supported the “big bang of deep learning.” At this time, many successful deep learning neural networks received training using Nvidia GPUs. GPUs have become remarkably important in machine learning. Deep learning algorithms are supported by neural networks.
J
jimstone-68634.medium.com
article
https://jimstone-68634.medium.com/a-very-short-history-of-artificial-neural-n…
The modern era of neural networks began in 1982 with the Hopfield net, shown in Figure 4. Although Hopfield nets are not practically very useful
E
en.wikipedia.org
article
https://en.wikipedia.org/wiki/History_of_artificial_neural_networks
* [(Top)](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#). * [3.1 LSTM](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#LSTM). * [5 Deep learning](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#Deep_learning). * [7.2 Transformer](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#Transformer). * [8.3 Deep learning](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#Deep_learning_2). * [11 Notes](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#Notes). * [Read](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks). * [Read](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks). popularized backpropagation.[[31]](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_note-32). They reported up to 70 times faster training.[[85]](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_note-86). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-fukuneoscholar_61-0)**Fukushima, K. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-wz1988_68-0)**Zhang, Wei (1988). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-wz1990_69-0)**Zhang, Wei (1990). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-70)**Fukushima, Kunihiko; Miyake, Sei (1982-01-01). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-LECUN1989_71-0)**LeCun _et al._, "Backpropagation Applied to Handwritten Zip Code Recognition," _Neural Computation_, 1, pp. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-73)**Zhang, Wei (1991). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-74)**Zhang, Wei (1994). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-Weng1992_75-0)**J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-Weng19932_76-0)**J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-Weng1997_77-0)**J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-81)**Sven Behnke (2003). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-:62_88-0)**Ciresan, D. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-:9_91-0)**Ciresan, D.; Meier, U.; Schmidhuber, J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-szegedy_94-0)**Szegedy, Christian (2015). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-101)**Linn, Allison (2015-12-10). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-olli2010_106-0)**Niemitalo, Olli (February 24, 2010). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-108)**Gutmann, Michael; Hyvärinen, Aapo. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-Cherry_1953_115-0)**Cherry EC (1953). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-118)**Fukushima, Kunihiko (1987-12-01). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-:12_121-0)**Soydaner, Derya (August 2022). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-122)**Giles, C. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-123)**Feldman, J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-125)**Schmidhuber, Jürgen (January 1992). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-135)**Levy, Steven. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-138)**Kohonen, Teuvo (1982). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-139)**Von der Malsburg, C (1973). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-141)**Smolensky, Paul (1986). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-144)**Sejnowski, Terrence J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-hinton2006_146-0)**[Hinton, G. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-hinton2009_147-0)**Hinton, Geoffrey (2009-05-31). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-149)**Watkin, Timothy L. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-150)**Schwarze, H; Hertz, J (1992-10-15). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-151)**Mato, G; Parga, N (1992-10-07). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-schmidhuber19922_153-0)**Schmidhuber, Jürgen (1992). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-154)**Hanson, Stephen; Pratt, Lorien (1988). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-157)**Yang, J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-158)**Strukov, D.
M
magazine.caltech.edu
research
https://magazine.caltech.edu/post/ai-machine-learning-history
In 1980, Hopfield left Princeton for Caltech in part due to the Institute’s “splendid computing facilities,” which he would use to test and develop his ideas for neural networks. “Hopfield extracted the essence of neurons.” Abu-Mostafa notes that the theoretical paper Hopfield published in 1982, “Neural networks and physical systems with emergent collective computational abilities,” is the fifth-most-cited Caltech paper of all time. His network was trained to dig a hole in the landscape corresponding to the image pattern being trained,” adds Erik Winfree (PhD ’98), professor of computer science, computation and neural systems, and bioengineering at Caltech, and a former CNS student of Hopfield’s. Even before Anandkumar joined Caltech in 2017, she says she “was fascinated by physics.” In 2011, she analyzed how the success of learning algorithms is tied to the phase transition in the Ising model, the same model upon which Hopfield built his network.
R
reddit.com
article
https://www.reddit.com/r/MachineLearning/comments/106ixxx/d_what_is_the_most_…
TIL the first neural network wasn't code, but a physical machine built in 1951 using parts from a B-24 bomber. Created by Marvin Minsky, the "