Category - Geoffrey Hinton

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 10.4 — Making full Bayesian learning practical [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 10.3 — The idea of full Bayesian learning [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 10.2 — Mixtures of Experts [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

10.1 — Why it helps to combine models [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 9.6 — MacKay ‘s quick and dirty method [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 9.5 — The Bayesian interpretation of weight decay [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 9.4 — Introduction to the full Bayesian approach [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 9.3 — Using noise as a regularizer [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 15.1 — From PCA to autoencoders [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 16.4 — The fog of progress [Neural Networks for Machine Learning]

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AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 16.3 — Bayesian optimization of hyper-parameters [Neural Networks for Machine Learning]