Sign inSign up
An Overview of Deep Learning and Its Challenges for Technical Computing thumbnail
Pause
Mute
Subtitles
Playback speed
0.25
0.5
0.75
1
1.25
1.5
1.75
2
Full screen

An Overview of Deep Learning and Its Challenges for Technical Computing

Published on Oct 13, 20147584 Views

Graham Taylor

TCMM 2014 - Leuven

Related categories

Machine LearningOptimization MethodsProgramming LanguagesPythonOpen-Source SoftwareMathematics

Chapter list

An overview of deep learning (and its challenges for technical computing) 00:00
Roadmap - 100:40
Roadmap - 200:41
Roadmap - 301:17
Roadmap - 401:27
Roadmap - 501:41
Learning Representations01:58
Deep Learning03:00
Deep Learning Timeline - 105:01
Deep Learning Timeline - 205:09
Deep Learning Timeline - 305:53
Deep Learning Timeline - 406:23
Deep Learning Timeline - 508:25
Example: Visual recognition - 108:59
Example: Visual recognition - 209:06
Example: Visual recognition - 309:09
Example: Visual recognition - 409:27
Feature Engineering09:55
What Limits Performance?10:11
Mid-level Representations12:11
Learning a Feature Hierarchy - 113:24
Learning a Feature Hierarchy - 214:13
Feature Hierarchies. So what?15:33
Feature Learning Paradigms17:09
Neural Networks (Introduction)17:40
Neural Networks for Supervised Learning18:41
Forward Propagation - 119:52
Forward Propagation - 220:45
Forward Propagation - 321:06
Alternative Graphical Representations21:19
How Good is a Network?22:21
Training24:21
Learning by Perturbing Weights25:01
The Idea behind Backpropagation26:44
Derivative w.r.t. Input of Softmax28:09
Backward Propagation - 229:27
Backward Propagation - 129:44
Backward Propagation - 329:47
Technical Challenge: Composition30:05
Tools for Building Neural Networks - 130:49
Tools for Building Neural Networks - 231:25
Caffe Example32:04
Caffe: Each layer defines… - 132:54
Caffe: Each layer defines… - 233:01
Caffe: Definition of a Net33:32
What about big nets?34:45
Technical Challenge: Computing Gradients35:40
Theano: teaser40:40
Technical Challenge: Optimization40:41
Technical Challenge: Hyperparameter Optimization41:46
Hyperparameter Optimization42:07
Hyperopt42:48
Spearmint44:21
Fully-Connected Layer45:03
Locally-Connected Layer - 146:17
Locally-Connected Layer - 246:59
Convolutional Layer - 147:20
Convolutional Layer - 248:07
Convolutional Layer - 348:54
Convolutional Layer - 449:27
Convolutional Layer - 550:10
Convolutional Layer - 650:26
Convolutional Net - Recap51:04
Pooling Layer - 151:46
Pooling Layer - 252:26
Types of Pooling52:41
Local Contrast Normalization - 153:11
Local Contrast Normalization - 253:46
Local Contrast Normalization - 353:54
Convnets: Single Stage54:29
Convnets: Typical Architecture55:16
Convnets: Training56:04
Convnets: Testing56:39
Convnets: today57:11
Technical Challenge: Scalability58:38
Convnets: why so successful now? - 159:38
Convnets: why so successful now? - 201:00:26
Tools: Scalability01:00:44
Motivation01:02:46
An Interesting Historical Fact01:03:49
Why Unsupervised Learning? - 101:04:52
Why Unsupervised Learning? - 201:05:27
Why Unsupervised Learning? - 301:06:01
Why Unsupervised Learning? - 401:07:52
Why Unsupervised Learning? - 501:09:08
Supervised Learning of Representations01:10:03
Unsupervised Learning of Representations - 101:10:20
Unsupervised Learning of Representations - 201:10:38
Principal Components Analysis01:12:18
An inefficient way to fit PCA01:13:41
Why fit PCA inefficiently?01:14:55
Auto-encoder - 101:16:09
Auto-encoder - 201:16:30
Regularized Auto-encoders01:16:32
Simple?01:16:46
Sparse Auto-encoders01:17:44
Denoising Auto-encoders01:18:43
Contractive Auto-encoders01:19:16
Stacking to Build Deep Models01:20:12
Stacking RBMs: Procedure - 101:20:53
Stacking RBMs: Procedure - 201:21:09
Stacking RBMs: Procedure - 301:21:27
Stacking RBMs: Procedure - 401:21:32
Deep Belief Networks01:21:52
Stacking RBMs: Intuition01:23:00
Conclusions and Challenges01:24:24