Lecture 16.3 — Bayesian optimization of hyper-parameters [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 16.3 — Bayesian optimization of hyper-parameters [Neural Networks for Machine Learning] канала Colin Reckons
Link to the course (login required): https://class.coursera.org/neuralnets-2012-001
Видео Lecture 16.3 — Bayesian optimization of hyper-parameters [Neural Networks for Machine Learning] канала Colin Reckons
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Thomas Huijskens - Bayesian optimisation with scikit-learn
Hyperparameter Optimization - The Math of Intelligence #7
Introduction to Bayesian Statistics - A Beginner's Guide![Lecture 4.3 — The softmax output function [Neural Networks for Machine Learning]](https://i.ytimg.com/vi/mlaLLQofmR8/default.jpg)
Lecture 4.3 — The softmax output function [Neural Networks for Machine Learning]
Scott Clark - Using Bayesian Optimization to Tune Machine Learning Models - MLconf SF 2016
Principles of Machine Learning | Nested Cross Validation![Lecture 1.3 — Some simple models of neurons [Neural Networks for Machine Learning]](https://i.ytimg.com/vi/iKKfoP-naN8/default.jpg)
Lecture 1.3 — Some simple models of neurons [Neural Networks for Machine Learning]![Lecture 16.3 — Bayesian optimization of hyper parameters — [ Deep Learning | Hinton | UofT ]](https://i.ytimg.com/vi/i0cKa0di_lo/default.jpg)
Lecture 16.3 — Bayesian optimization of hyper parameters — [ Deep Learning | Hinton | UofT ]
Bayes theorem, the geometry of changing beliefs
Machine learning - Bayesian optimization and multi-armed bandits
Hyperparameter Tuning in Practice (C2W3L03)
Geoffrey Hinton: The Foundations of Deep Learning
2. Bayesian Optimization
How optimization for machine learning works, part 1
How To Update Your Beliefs Systematically - Bayes’ Theorem
Zi Wang - Bayesian Optimization for Global Optimization of Expensive Black-box Functions
Neural Networks Explained - Machine Learning Tutorial for Beginners
Michael Osborne: Bayesian Optimisation is Probabilistic Numerics
TutORial: Bayesian Optimization