Category - Geoffrey Hinton

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 2.5 — What perceptrons can’t do [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 10.5 — Dropout [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 12.5 — RBMs for collaborative filtering [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 12.4 — An example of RBM learning [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 12.2 — More efficient ways to get the statistics [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 12.3 — Restricted Boltzmann Machines [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 12.1 — Boltzmann machine learning [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 11.5 — How a Boltzmann machine models data [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 11.4 — Using stochastic units to improve search [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 11.3 — Hopfield nets with hidden units [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 11.2 — Dealing with spurious minima [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 11.1 — Hopfield Nets [Neural Networks for Machine Learning]