Загрузка страницы

Lecture 15.3 — Deep autoencoders for document retrieval [Neural Networks for Machine Learning]

Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012.

Link to the course (login required): https://class.coursera.org/neuralnets-2012-001

Видео Lecture 15.3 — Deep autoencoders for document retrieval [Neural Networks for Machine Learning] канала Colin Reckons
Показать
Комментарии отсутствуют
Введите заголовок:

Введите адрес ссылки:

Введите адрес видео с YouTube:

Зарегистрируйтесь или войдите с
Информация о видео
5 февраля 2016 г. 12:33:32
00:08:19
Другие видео канала
Re-configuring genetic circuits with lightRe-configuring genetic circuits with lightLecture 5.2 — Achieving viewpoint invariance  [Neural Networks for Machine Learning]Lecture 5.2 — Achieving viewpoint invariance [Neural Networks for Machine Learning]Lecture 14.5 — RBMs are infinite sigmoid belief nets  [Neural Networks for Machine Learning]Lecture 14.5 — RBMs are infinite sigmoid belief nets [Neural Networks for Machine Learning]10.1 — Why it helps to combine models  [Neural Networks for Machine Learning]10.1 — Why it helps to combine models [Neural Networks for Machine Learning]Bob Kahn -- Vannevar Bush Symposium 1995Bob Kahn -- Vannevar Bush Symposium 1995Doug Engelbart - Turing Award Lecture 1998Doug Engelbart - Turing Award Lecture 1998Lecture 7.3 — A toy example of training an RNN  [Neural Networks for Machine Learning]Lecture 7.3 — A toy example of training an RNN [Neural Networks for Machine Learning]Lecture 8.3 — Predicting the next character using HF  [Neural Networks for Machine Learning]Lecture 8.3 — Predicting the next character using HF [Neural Networks for Machine Learning]Lecture 16.2 — Hierarchical Coordinate Frames  [Neural Networks for Machine Learning]Lecture 16.2 — Hierarchical Coordinate Frames [Neural Networks for Machine Learning]Lecture 13.3 — Learning sigmoid belief nets  [Neural Networks for Machine Learning]Lecture 13.3 — Learning sigmoid belief nets [Neural Networks for Machine Learning]Lecture 3.5 — Using the derivatives from backpropagation  [Neural Networks for Machine Learning]Lecture 3.5 — Using the derivatives from backpropagation [Neural Networks for Machine Learning]Lecture 4.4 — Neuro-probabilistic language models  [Neural Networks for Machine Learning]Lecture 4.4 — Neuro-probabilistic language models [Neural Networks for Machine Learning]Lecture 15.5 — Learning binary codes for image retrieval  [Neural Networks for Machine Learning]Lecture 15.5 — Learning binary codes for image retrieval [Neural Networks for Machine Learning]Lecture 13.2 — Belief Nets  [Neural Networks for Machine Learning]Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning]Professor Martin Campbell Kelly - From World Brain to the World Wide WebProfessor Martin Campbell Kelly - From World Brain to the World Wide WebLecture 5.1 — Why object recognition is difficult  [Neural Networks for Machine Learning]Lecture 5.1 — Why object recognition is difficult [Neural Networks for Machine Learning]Lecture 15.4 — Semantic Hashing  [Neural Networks for Machine Learning]Lecture 15.4 — Semantic Hashing [Neural Networks for Machine Learning]Lecture 15.6 — Shallow autoencoders for pre-training  [Neural Networks for Machine Learning]Lecture 15.6 — Shallow autoencoders for pre-training [Neural Networks for Machine Learning]Lecture 9.1 — Overview of ways to improve generalization  [Neural Networks for Machine Learning]Lecture 9.1 — Overview of ways to improve generalization [Neural Networks for Machine Learning]Lecture 9.5 — The Bayesian interpretation of weight decay  [Neural Networks for Machine Learning]Lecture 9.5 — The Bayesian interpretation of weight decay [Neural Networks for Machine Learning]Lecture 11.4 — Using stochastic units to improve search  [Neural Networks for Machine Learning]Lecture 11.4 — Using stochastic units to improve search [Neural Networks for Machine Learning]
Яндекс.Метрика