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towardsai.net
article
https://towardsai.net/p/l/a-brief-history-of-neural-nets
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. ##### LAI #122: Word Embeddings Started in 1948, Not With Word2Vec. AI Algorithms Analytics Artificial Intelligence Big Data Business Chatgpt Classification Computer Science computer vision Data Data Analysis Data Science Data Visualization Deep Learning education Finance Generative Ai Image Processing Innovation Large Language Models Linear Regression Llm machine learning Mathematics Mlops Naturallanguageprocessing Neural Networks NLP OpenAI Pandas Programming Python research science Software Development Startup Statistics technology Tensorflow Thesequence Towards AI Towards AI - Medium Towards AI — Multidisciplinary Science Journal - Medium Transformers.
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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.
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thenewstack.io
news
https://thenewstack.io/the-50-year-story-of-the-rise-fall-and-rebirth-of-neur…
These are called *neural networks.* As the 1960s came to an end, bottom-up artificial intelligence researchers focused on creating artificial neural networks. In a book he wrote with Seymour Papert called *Perceptrons*, Minsky proved that a perceptron, the name given to an early type of artificial neural network, could not learn how to carry out an exclusive or (XOR), a severe limitation for any computing system. A technique called *backpropagation of errors* made artificial neural networks easier to train. Partly because of methods such as back propagation of errors and partly because of the continuing effects of Moore’s law, a company called DeepMind trained an artificial neural network to play *Breakout*. I also knew that a computer would never beat Go because I “knew” top-down AI was a dead end and that there would never be enough computing power to train artificial neural networks. After DeepMind created their *Breakout* playing artificial intelligence, they created AlphaGo. DeepMind’s researchers trained AlphaGo on a number of human games.
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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.
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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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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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diplomacy.edu
research
https://www.diplomacy.edu/blog/origins-of-ai-from-neurons-to-neural-networks/
Diplo/GIP at AI for Good Global Summit. # Origins of AI: From neurons to neural networks. While neural networks dominate today’s AI headlines, there are several other approaches to building intelligent systems that don’t rely on deep learning architectures:. ## **Understanding modern AI: Deep learning and transformers**. ### AI as a factor in diplomacy and geopolitics. In the global rush to regulate Artificial Intelligence, a dangerous consensus seemingly has formed: that the real drama of AI lies in the future – in existential risks, deepfakes, or algorithmic bia[...]. When India hosted the AI Impact Summit in New Delhi, from 16-20 February, it seized the moment to demonstrate its growing influence in the digital and AI field. ### AI Summit in Geneva: Ten ways Switzerland can contribute to AI and humanity. Tailor your subscription to your interests, from updates on the dynamic world of digital diplomacy to the latest trends in AI.
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sidecar.ai
article
https://sidecar.ai/blog/the-evolution-of-neural-networks-and-their-powerful-r…
Artificial Intelligence AI Neural Network. The primary function of neural networks in AI is to recognize patterns, make predictions, and solve complex problems that involve vast amounts of data and intricate computations. Neural networks are composed of layers of interconnected neurons, each playing a crucial role in the network's ability to process information. Deep neural networks, which contain many hidden layers, are capable of learning complex patterns and representations of data, making them particularly effective for tasks such as image and speech recognition. ## Training Neural Networks. The process of training neural networks is crucial for their ability to perform tasks accurately. The training process requires a large amount of data to be effective, as neural networks learn patterns and relationships within the data. As neural networks become more complex, with deeper architectures and larger datasets, the training process can become computationally intensive and time-consuming. ## Neural Networks and Deep Learning. The relationship between neural networks and deep learning is integral to the advancements in AI.