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  1. Lecture slides from the 2012 Coursera course: Neural Networks for Machine Learning. lecture1.pptx &nbsp &nbsp &nbsp lecture1.pdf lecture2.pptx &nbsp &nbsp &nbsp lecture2.pdf lecture3.pptx &nbsp &nbsp &nbsp lecture3.pdf lecture4.pptx &nbsp &nbsp &nbsp lecture4.pdf lecture5.pptx &nbsp &nbsp &nbsp lecture5.pdf lecture6.pptx &nbsp &nbsp &nbsp ...

  2. Deep learning for AI. Communications of the ACM, 64 (7), 58-65. [ pdf] 2021 commencement address at IIT Mumbai. Joseph Turian's map of 2500 English words produced by using t-SNE on the word feature vectors learned by Collobert & Weston, ICML 2008.

  3. Geoffrey E. Hinton Neural Network Tutorials. COMPANIES. AT&T Bell Labs (2 day), 1988 ; Apple (1 day), 1990; Digital Equipment Corporation (2 day), 1990; Government of Canada (2 day), 1994; PUBLIC. A two-day intensive Tutorial on Advanced Learning Methods. Presented by Geoffrey Hinton and Michael Jordan

  4. Oct 27, 2023 · Visionary Thinkers: Geoffrey Hinton, “Will digital intelligence replace biological intelligence?”. Friday, October 27, 2023. 7:30 PM. The Schwartz Reisman Institute for Technology and Society and the Department of Computer Science at the University of Toronto, in collaboration with the Vector Institute for Artificial Intelligence and the ...

  5. Feb 20, 2024 · Professor Hintons work on neural networks shaped the AI systems of today. He was one of the researchers who introduced the backpropagation algorithm and the first to use backpropagation for learning word embeddings.

  6. Presentation slides for AI 101 - RES.6-013. AI 101. By Brandon Leshchinskiy. Watch Mashable video about Google’s AI-based personal assistant: https://www.youtube.com/watch?v=JvbHu_bVa_g. machine learning can solve many problems. But finding the right data and training the right model can be difficult. AI. ML. Deep Learning.

  7. We designed the model we present in this paper, based on the the needs introduced by new trends in neural computing. We created a model for information processing inside neurons. It is constructed, from a neural inside point of view, as a feedback system that controls the flow of information passing through the neuron.

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