Video AI Science, Neural Network Gradient descent, how neural networks learn | Chapter 2, Deep learning
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 2.5 — What perceptrons can’t do [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 9.5 — The Bayesian interpretation of weight decay [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 12.5 — RBMs for collaborative filtering [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 12.4 — An example of RBM learning [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 12.2 — More efficient ways to get the statistics [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 12.3 — Restricted Boltzmann Machines [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 12.1 — Boltzmann machine learning [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 11.5 — How a Boltzmann machine models data [Neural Networks for Machine Learning]