Introduction to Deep Learning CMPT 733. Steven Bergner
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1 Introduction to Deep Learning CMPT 733 Steven Bergner
2 Overview Renaissance of artificial neural networks Representation learning vs feature engineering Background Linear Algebra, Optimization Regularization Construction and training of layered learners Frameworks for deep learning 2
3 Representations matter Transform into the right representation Classify points simply by threshold on radius axis 3
4 Representations matter Transform into the right representation Classify points simply by threshold on radius axis Single neuron with nonlinearity can do this 4
5 Depth: layered composition 5
6 Computational graph 6
7 Components of learning Hand designed program Input Output Increasingly automated Simple features Abstract features Mapping from features 7
8 Growing Dataset Size MNIST dataset 8
9 Basics Linear Algebra and Optimization 9
10 Linear Algebra Tensor is an array of numbers Multi-dim: 0d scalar, 1d vector, 2d matrix/image, 3d RGB image Matrix (dot) product Dot product of vectors A and B (m = p = 1 in above notation, n=2) 10
11 Linear Algebra Tensor is an array of numbers Multi-dim: 0d scalar, 1d vector, 2d matrix/image, 3d RGB image Matrix (dot) product Dot product of vectors A and B (m = p = 1 in above notation, n=2) 11
12 Linear Algebra Tensor is an array of numbers Multi-dim: 0d scalar, 1d vector, 2d matrix/image, 3d RGB image Matrix (dot) product Dot product of vectors A and B (m = p = 1 in above notation, n=2) 12
13 Linear Algebra Tensor is an array of numbers Multi-dim: 0d scalar, 1d vector, 2d matrix/image, 3d RGB image Matrix (dot) product Dot product of vectors A and B (m = p = 1 in above notation, n=2) 13
14 Linear algebra: Norms 14
15 Nonlinearities ReLU Sofplus Logistic Sigmoid [(c) public domain] 15
16 Approximate Optimization 16
17 Gradient descent 17
18 Critical points 18
19 Critical points Saddle point 1st and 2nd derivative vanish 19
20 Critical points Saddle point 1st and 2nd derivative vanish Poor conditioning: 1st deriv large in one and small in another direction 20
21 Tensorflow Playground Try out simple network configurations ssify2d.html Visualize linear and non-linear mappings 21
22 Regularization Reduced generalization error without impacting training error 22
23 Constrained optimization Unregularized objective 23
24 Constrained optimization Squared L2 encourages small weights Unregularized objective L2 regularizer 24
25 Constrained optimization Squared L2 encourages small weights Unregularized objective L1 encourages sparsity of model parameters (weights) L2 regularizer 25
26 Dataset augmentation 26
27 Learning curves 27
28 Learning curves Early stopping before validation error starts to increase 28
29 Bagging Average multiple models trained on subsets of the data 29
30 Bagging Average multiple models trained on subsets of the data First subset: learns top loop, Second subset: bottom loop 30
31 Dropout Random sample of connection weights is set to zero Train diferent network model each time Learn more robust, generalizable features 31
32 Multitask learning Shared parameters are trained with more data Improved generalization error due to increased statistical strength 32
33 Components of popular architectures 33
34 Convolution as edge detector 34
35 Gabor wavelets (kernels) 35
36 Gabor wavelets (kernels) Local average, first derivative 36
37 Gabor wavelets (kernels) Second derivative (curvature) Local average, first derivative 37
38 Gabor wavelets (kernels) Directional second derivative Second derivative (curvature) Local average, first derivative 38
39 Gabor-like learned kernels Features extractors provided by pretrained networks 39
40 Max pooling translation invariance Take max of certain neighbourhood 40
41 Max pooling translation invariance Take max of certain neighbourhood Ofen combined followed by downsampling 41
42 Max pooling transform invariance 42
43 Types of connectivity 43
44 Types of connectivity 44
45 Types of connectivity 45
46 Choosing architecture family 46
47 Choosing architecture family No structure fully connected 47
48 Choosing architecture family No structure fully connected Spatial structure convolutional 48
49 Choosing architecture family No structure fully connected Spatial structure convolutional Sequential structure recurrent 49
50 Optimization Algorithm Lots of variants address choice of learning rate See Visualization of Algorithms AdaDelta and RMSprop ofen work well 50
51 Sofware for Deep Learning 51
52 Current Frameworks Tensorflow / Keras Pytorch DL4J Cafe And many more Most have CPU-only mode but much faster on NVIDIA GPU 52
53 Development strategy Identify needs: High accuracy or low accuracy? Choose metric Accuracy (% of examples correct), Coverage (% examples processed) Precision TP/(TP+FP), Recall TP/(TP+FN) Amount of error in case of regression Build end-to-end system Start from baseline, e.g. initialize with pre-trained network Refine driven by data 53
54 Sources I. Goodfellow, Y. Bengio, A. Courville Deep Learning MIT Press 2016 [link] 54
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