lecture 16.3 — bayesian optimization of hyper parameters — [ deep learning | hinton | uoft ]

Published 6 years ago • 19K plays • Length 13:30

Download video MP4
Download video MP3

Similar videos

10:50

lecture 9.4 — introduction to the full bayesian approach — [ deep learning | hinton | uoft ]
13:30

lecture 16.3 — bayesian optimization of hyper-parameters [neural networks for machine learning]
13:30

lecture 16c : bayesian optimization of neural network hyperparameters
9:50

bayesian optimization (bayes opt): easy explanation of popular hyperparameter tuning method
10:53

lecture 9.5 — the bayesian interpretation of weight decay — [ deep learning | hinton | uoft ]
2:25

lecture 16.4 — the fog of progress — [ deep learning | geoffrey hinton | uoft ]
9:41

lecture 16.2 — hierarchical coordinate frames — [ deep learning | geoffrey hinton | uoft ]
7:58

lecture 15.1 — from pca to autoencoders — [ deep learning | geoffrey hinton | uoft ]
17:12

lecture 14.5 — rbms are infinite sigmoid belief nets — [ deep learning | geoffrey hinton | uoft ]
7:21

lecture 4.3 — the softmax output function — [ deep learning | geoffrey hinton | uoft ]
3:40

geoffrey hinton: “self-driving vehicles will be much safer than current vehicles”
11:25

bayesian hyperparameter optimization for keras (8.4)
57:30

deep learning 2.0: how bayesian optimization may power the next generation of dl by frank hutter
43:22

using bayesian optimization to tune deep learning pipelines by scott clark
43:50

scipy 2013 keynote: trends in machine learning and the scipy community
36:09

towards automating machine learning - dr thorben jensen
15:26

bayesian optimization with gradients (nips 2017 oral)
27:47

11. deep neural networks: a bayesian perspective. dmitry vetrov
47:31

cnn | optimization(part-1) | machine learning (inf8245e) | lecture-11 | part-1
17:35

lecture 14.1 — learning layers of features by stacking rbms — [ deep learning | hinton | uoft ]
1:22:26

deep learning -- yoshua bengio (part 3)

Clip.africa.com - Privacy-policy