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From neural networks to deep learning

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- 1. From Artiﬁcial Neural Networks to Deep learning Viet-Trung Tran 1
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- 5. 5
- 6. Perceptron • Rosenblatt 1957 • input signals x1, x2, • bias x0 = 1 • Net input = weighted sum = Net(w,x) • Activation/transfer func = f(Net(w,x)) • output weighted sum step func1on 6
- 7. Weighted Sum and Bias • Weighted sum • Bias 7
- 8. 8
- 9. Hard-limiter function • Hard-limiter – Threshold function – Discontinuous function – Discontinuous derivative 9
- 10. Threshold logic function • Saturating linear function • Contiguous function • Discontinuous derivative 10
- 11. Sigmoid function • Most popular • Output (0,1) • Continuous derivatives • Easy to diﬀerentiate 11
- 12. Artiﬁcial neural network – ANN structure • Number of input/output signals • Number of hidden layers • Number of neurons per layer • Neuron weights • Topology • Biases 12
- 13. Feed-forward neural network • connections between the units do not form a directed cycle 13
- 14. Recurrent neural network • A class of artiﬁcial neural network where connections between units form a directed cycle 14
- 15. Why hidden layers 15
- 16. Neural network learning • 2 types of learning – Parameter learning • Learn neuron weight connections – Structure learning • Learn ANN structure from training data 16
- 17. Error function • Consider an ANN with n neurons • For each learning example (x,d) – Training error caused by current weight w • Training error caused by w for entire learning examples 17
- 18. Learning principle 18
- 19. Neuron error gradients 19
- 20. Parameter learning: back propagation of error • Calculate total error at the top • Calculate contributions to error at each step going backwards 20
- 21. Back propagation discussion • Initial weights • Learning rate • Number of neurons per hidden layers • Number of hidden layers 21
- 22. Stochastic gradient descent (SGD) 22
- 23. 23
- 24. Deep learning 24
- 25. Google brain 25
- 26. GPU 26
- 27. Learning from tagged data • @Andrew Ng 27
- 28. 2006 breakthrough • More data • Faster hardware: GPU’s, multi-core CPU’s • Working ideas on how to train deep architectures 28
- 29. 29
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- 31. 31
- 32. Deep Learning trends • @Andrew Ng 32
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- 35. AI will transform the internet • @Andrew Ng • Technology areas with potential for paradigm shift: – Computer vision – Speech recognition & speech synthesis – Language understanding: Machine translation; Web search; Dialog systems; …. – Advertising – Personalization/recommendation systems – Robotics • All this is hard: scalability, algorithms. 35
- 36. 36
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- 38. 38
- 39. Deep learning 39
- 40. 40
- 41. CONVOLUTIONAL NEURAL NETWORK http://colah.github.io/ 41
- 42. Convolution • Convolution is a mathematical operation on two functions f and g, producing a third function that is typically viewed as a modiﬁed version of one of the original functions, 42
- 43. Convolutional neural networks • Conv Nets is a kind of neural network that uses many identical copies of the same neuron – Large number of neurons – Large computational models – Number of actual weights (parameters) to be learned fairly small 43
- 44. A 2D Convolutional Neural Network • a convolutional neural network can learn a neuron once and use it in many places, making it easier to learn the model and reducing error. 44
- 45. Structure of Conv Nets • Problem – predict whether a human is speaking or not • Input: audio samples at diﬀerent points in time 45
- 46. Simple approach • just connect them all to a fully-connected layer • Then classify 46
- 47. A more sophisticated approach • Local properties of the data – frequency of sounds (increasing/decreasing) • Look at a small window of the audio sample – Create a group of neuron A to compute certain features – the output of this convolutional layer is fed into a fully- connected layer, F 47
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- 50. Max pooling layer 50
- 51. 2D convolutional neural networks 51
- 52. 52
- 53. 53
- 54. Three-dimensional convolutional networks 54
- 55. Group of neurons: A • Bunch of neurons in parallel • all get the same inputs and compute diﬀerent features. 55
- 56. Network in Network (Lin et al. (2013) 56
- 57. Conv Nets breakthroughs in computer vision • Krizehvsky et al. (2012) 57
- 58. Diferent Levels of Abstraction 58
- 59. 59
- 60. 60
- 61. RECURRENT NEURAL NETWORKS http://colah.github.io/ 61
- 62. Recurrent Neural Networks (RNN) have loops • A loop allows information to be passed from one step of the network to the next. 62
- 63. Unroll RNN • recurrent neural networks are intimately related to sequences and lists. 63
- 64. Examples • predict the last word in “the clouds are in the sky" • the gap between the relevant information and the place that it’s needed is small • RNNs can learn to use the past information 64
- 65. • “I grew up in France… I speak ﬂuent French.” • As the gap grows, RNNs become unable to learn to connect the information. 65
- 66. LONG SHORT TERM MEMORY NETWORKS LSTM Networks 66
- 67. LSTM networks • A Special kind of RNN • Capable of learning long-term dependencies • Structure in the form of a chain of repeating modules of neural network 67
- 68. RNN • repeating module has a very simple structure, such as a single tanh layer 68
- 69. • The tanh(z) function is a rescaled version of the sigmoid, and its output range is [ − 1,1] instead of [0,1]. 69
- 70. LSTM networks • Repeating module consists of four neuron, interacting in a very special way 70
- 71. Core idea behind LSTMs • The key to LSTMs is the cell state, the horizontal line running through the top of the diagram. • The cell state runs straight down the entire chain, with only some minor linear interactions • Easy for information to just ﬂow along it unchanged 71
- 72. Gates • The ability to remove or add information to the cell state, carefully regulated by structures called gates • Sigmoid – How much of each component should be let through. – Zero means nothing through – One means let everything through • An LSTM has three of these gates 72
- 73. LSTM step 1 • decide what information we’re going to throw away from the cell state • forget gate layer 73
- 74. LSTM step 2 • decide what new information we’re going to store in the cell state • input gate layer 74
- 75. LSTMs step 3 • update the old cell state, Ct−1, into the new cell state Ct 75
- 76. LSTMs step 4 • decide what we’re going to output 76
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- 80. 80
- 81. RECURRENT NEURAL NETWORKS WITH WORD EMBEDDINGS 81
- 82. APPENDIX 82
- 83. 83
- 84. Perceptron 1957 84
- 85. Perceptron 1957 85
- 86. Perceptron 1986 86
- 87. Perceptron 87
- 88. Activation function 88
- 89. Back propagation 1974/1986 89
- 90. 90
- 91. 91
- 92. • Inspired by the architectural depth of the brain, researchers wanted for decades to train deep multi- layer neural networks. • No successful attempts were reported before 2006 …Exception: convolutional neural networks, LeCun 1998 • SVM: Vapnik and his co-workers developed the Support Vector Machine (1993) (shallow • architecture). • Breakthrough in 2006! 92
- 93. 2006 breakthrough • More data • Faster hardware: GPU’s, multi-core CPU’s • Working ideas on how to train deep architectures 93
- 94. • Beat state of the art in many areas: – Language Modeling (2012, Mikolov et al) – Image Recognition (Krizhevsky won 2012 ImageNet competition) – Sentiment Classiﬁcation (2011, Socher et al) – Speech Recognition (2010, Dahl et al) – MNIST hand-written digit recognition (Ciresan et al, 2010) 94
- 95. Credits • Roelof Pieters, www.graph-technologies.com • Andrew Ng • http://colah.github.io/ 95

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