Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 16.4 — The fog of progress [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 16.3 — Bayesian optimization of hyper-parameters [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 16.2 — Hierarchical Coordinate Frames [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 16.1 — Learning a joint model of images and captions [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 15.6 — Shallow autoencoders for pre-training [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 15.5 — Learning binary codes for image retrieval [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 15.4 — Semantic Hashing [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 15.3 — Deep autoencoders for document retrieval [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 13.1 — The ups and downs of backpropagation [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 15.1 — From PCA to autoencoders [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 14.5 — RBMs are infinite sigmoid belief nets [Neural Networks for Machine Learning]
Video AI Science, Geoffrey Hinton, Neural Network, University AI Lecture 14.4 — Modeling real valued data with an RBM [Neural Networks for Machine Learning]
