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flagshippioneering.com
article
https://www.flagshippioneering.com/timelines/a-timeline-of-deep-learning
Research on neural networks stalls after MIT’s Marvin Minsky and Seymour Papert argue, in a book called “Perceptrons,” that the method would be too limited to be useful even if neural networks had many more layers of artificial neurons than Rosenblatt’s machine did. The backpropagation algorithm had been applied in computers in the 1970s, but now researchers put it to wider use in neural networks. Google researcher Ian Goodfellow plays two neural networks off each other to create what he calls a “generative adversarial network.” One network is programmed to generate data—such as an image of a face—while the other, known as the discriminator, evaluates whether it’s plausibly real. A deep learning system called AlphaGo beats human Go champion Lee Sedol after absorbing thousands of examples of past games played by people. The same team develops AlphaFold, a set of deep learning and generative neural networks to predict the structure of proteins from their amino acid sequences.
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researchgate.net
research
https://www.researchgate.net/figure/Timeline-of-the-history-of-artificial-neu…
Timeline of the history of artificial neural networks and deep learning. Deep learning's peak corresponds with Hinton's et al breakthrough paper [50] and
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pub.towardsai.net
article
https://pub.towardsai.net/a-brief-history-of-neural-nets-472107bc2c9c
They developed a simple neural network using electrical circuits to show how neurons in the brain might work. * **1958:**_Frank Rosenblatt_ develops the _perceptron_ (single-layer neural network) inspired by the way neurons work in the brain. * **1982:**_John_ _Hopfield_ develops the Hopfield Network, a recurrent Neural Net, which describes relationships between binary (firing or not-firing) neurons. * **1998:**_LeNet_-5 — a Convolutional Neural Network was developed by _Yann_ _LeCun et al.._ Convolutional Neural Nets are especially suited for image data. * **2006:**_Geoffrey Hinton_ creates the _Deep Belief Network_, a generative model. * **2009:**_Ruslan Salakhutdinov_ and _Geoffrey Hinton_ present _Deep Boltzmann Machine_, a generative model similar to a Deep Belief Network, but allowing bidirectional in the bottom layer. The U-Net consists of a encoder convolutional network connected with a decoder network to upsample the image. * **2020**: _OpenAI_ publishes Generative Pre-trained Transformer 3 (GPT-3), a deep learning model to produce human-like text. Image 24: AI Agents: Complete Course.
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sebastianraschka.com
article
https://sebastianraschka.com/pdf/lecture-notes/stat453ss21/L02_dl-history_sli…
Image source: https://www.researchgate.net/profile/Alexander_Magoun2/ publication/265789430/figure/fig2/AS:392335251787780@1470551421849/ ADALINE-An-adaptive-linear-neuron-Manually-adapted-synapses-Designed-and-built-by-Ted.png Neural Networks and Deep Learning -- A Timeline Sebastian Raschka STAT 453: Intro to Deep Learning 7 Widrow and Hoff's ADALINE (1960) A nicely differentiable neuron model Widrow, B., & Hoff, M. Sebastian Raschka STAT 453: Intro to Deep Learning 30 Graph neural networks (A gentle introduction to graph neural networks: https://heartbeat.fritz.ai/introduction-to-graph-neural-networks-c5a9f4aa9e99) Sebastian Raschka STAT 453: Intro to Deep Learning 31 Large-scale language models Model sizes of language models from 2018–2020 (Credit: State of AI Report 2020) https://ruder.io/research-highlights-2020/ Sebastian Raschka STAT 453: Intro to Deep Learning 32 https://arxiv.org/abs/2101.01169 "Transformer is data-hungry in nature e.g., a large- scale dataset like ImageNet [14 million images] is not enough to train vision transformer from scratch so [10] proposes to ..." Sebastian Raschka STAT 453: Intro to Deep Learning 33 Next Lecture: The Perceptron Sebastian Raschka STAT 453: Intro to Deep Learning 34 Important: Homework for next lecture (ungraded) As preparation for next lecture https://sebastianraschka.com/blog/2020/numpy-intro.html
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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
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cs.stanford.edu
research
https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks…
| The Artificial Neuron History Comparison Architecture Applications Future Sources | Neural Network Header **History: The 1940's to the 1970's** In 1943, neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper on how neurons might work. In order to describe how neurons in the brain might work, they modeled a simple neural network using electrical circuits. MADALINE was the first neural network applied to a real world problem, using an adaptive filter that eliminates echoes on phone lines. It is based on the idea that while one active perceptron may have a big error, one can adjust the weight values to distribute it across the network, or at least to adjacent perceptrons. Despite the later success of the neural network, traditional von Neumann architecture took over the computing scene, and neural research was left behind. In the same time period, a paper was written that suggested there could not be an extension from the single layered neural network to a multiple layered neural network.
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medium.com
article
https://medium.com/@tlhumphrey2/the-history-of-neural-networks-and-large-lang…
# The History of Neural Networks and Large Language Models: 1950s to Present | by Timothy Lee Humphrey | Medium. Image 2: Timothy Lee Humphrey. From the early perceptron to today’s massive foundation models, the history of neural networks and large language models is a story of persistence, innovation, and exponential growth. The story of neural networks begins in the 1950s, at the intersection of neuroscience and early computer science. Image 3: Timothy Lee Humphrey. Image 4: Timothy Lee Humphrey. Image 7: Timothy Lee Humphrey. Image 8: New Version: Using Microsoft Copilot to Create a Python Program that Uses OpenAI. Image 9: Timothy Lee Humphrey. Image 11: Timothy Lee Humphrey. Image 12: Building a Human-Like AI: The Path to Consciousness, Self-Awareness, and Emotion. Image 13: Timothy Lee Humphrey. Image 14: If You Understand These 5 AI Terms, You’re Ahead of 90% of People. Image 16: AI Agents: Complete Course. Image 20: I Tried Gemma 4 On Claude Code (And Found New FREE Google Coding Beast).
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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.