Machine Learning. Boris

Transcription

1 Machine Learning Boris

2 @borisnadion

3 astrails

4 awesome web and mobile apps since 2005

5

6

7

8

9

10

11 terms

12 AI (artificial intelligence) - the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages

13 ML (machine learning) - is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

14 without being explicitly programmed

15 FF NN cost function

16 FF NN Cost Function I m kidding

17 cost function with regularization

18 2 types of ML supervised learning unsupervised learning

19 supervised the training data is labeled, eg. we know the correct answer

20 unsupervised the training data is not labeled, eg. we would figure out hidden correlations by ourselves

21 linear regression supervised learning

22 y (i) (x (1), y (1) ), (x (2), y (2) ) (x (m), y (m) ) m training examples of (x (i), y (i) ) x (i) - feature y (i) - label x (i)

23 training set learning algorithm (new data) x y h θ (x) (prediction)

24 training set learning algorithm (new data) x y h θ (x) (prediction)

25 h θ (x) = hypothesis

26 y = h θ (x) = θ 0 + θ 1 x (x, y) find θ 0 and θ 1

27 h θ (x) = θ 0 + θ 1 x 1 + θ 2 x θ n x n many features, n - number of features

28 size, sq.m x1 # rooms x2 age x3 price y M M M M

29 1 USD = 3.85 NIS

30 h θ (x) = θ 0 + θ 1 x 1 summate the prediction error on training set

31 Linear Regression Cost Function

32 minimize J(θ) funding a minimum of cost function = learning

33 gradient descent batch, stochastic, etc, or advanced optimization algorithms to find a global (sometimes local) minimum of cost function J α - learning rate, a parameter of gradient descent

34 (x (1), y (1) ), (x (2), y (2) ) (x (m), y (m) ) gradient descent magic inside θ 0, θ 1, θ 2,, θ n

35 h θ (x) = θ 0 + θ 1 x 1 + θ 2 x θ n x n we re ready to predict

36 features scaling 0 x 1

37 size, sq.m size, sq.m / 110 x

38 mean normalization average value of the feature is ~0-0.5 x 0.5

39 size, sq.m (size, sq.m / 110) x

40 matrix manipulations X = n x 1 vector, ϴ = n x 1 vector h θ (x) = θ 0 + θ 1 x 1 + θ 2 x θ n x n h θ (x) = ϴ T X

41 GPU

42

43 logistic regression supervised learning

44 classifier

45 y = 0, false y = 1, true

46 h θ (x) = g(θ T X) h θ (X) - estimated probability that y = 1 on input X g(z) - logistic non-linear function

47 logistic function g(z) there is a few: sigmoid, tahn, ReLUs, etc image source: Wikipedia

48 (x (1), y (1) ), (x (2), y (2) ) (x (m), y (m) ) y = {0, 1} minimize the cost function vector θ

49 training set learning algorithm (new data) x y h θ (x) (prediction) h θ (x) = g(θ T X) y true y < false

50 one-vs-all supervised learning

51

52 y = 0, false y = 1, true y = 0, false

53 don t implement it at home use libsvm, liblinear, and others

54 neural networks supervised learning

55 neuron a 0 a 1 computation h θ (a) a 2

56 feed forward neural network output layer input layer hidden layer

57 estimates size, sq.m # rooms age e 0 e 1 e 2 e 3 estimates final estimate

58 multiclass classifiers

59 logistic unit x 0 θ 1 θ 2 θ 3 h θ = g(x 0 θ 0 + x 1 θ 1 + x 2 θ 2 ) x 1 x 2 θ - weights g - activation function

60 logistic function g(z) there is a few: sigmoid, tahn, ReLUs, etc image source: Wikipedia

61 output: probabilities that y = that y = 2

62 net with no hidden layers no hidden layers = one-vs-all logistic regression

63 cost function sometimes called loss function of NN, a representation of an error between a real and a predicted value

64 training set learning algorithm (new data) x y θ (prediction)

65 backprop backward propagation of errors

66 gradient descent + backprop deep learning - is training a neural net deep - because we have many layers

67 convolutional neural nets widely used for image processing and object recognition

68 recurrent neural nets widely used for natural language processing

69 CPU/GPU expensive

70 image source:

71 2008 image source:

72 2016

73 destination suggestion

74 tangledpath/ruby-fann Ruby library for interfacing with FANN (Fast Artificial Neural Network)

75 require './neural_network' LOCATIONS = [:home, :work, :tennis, :parents] LOCATIONS_INDEXED = LOCATIONS.map.with_index { x, i [x, i] }.to_h XX = [ # week 1 # 1st day of week, 8am [:work, 1, 8], [:tennis, 1, 17], [:home, 1, 20], [:work, 2, 8], [:home, 2, 18], [:work, 3, 8], [:tennis, 3, 17], [:home, 3, 20], [:work, 4, 8], [:home, 4, 18], [:work, 5, 8], [:home, 5, 18], [:parents, 7, 13], [:home, 7, 18], # week 2 [:work, 1, 8], [:home, 1, 18], [:work, 2, 8], [:home, 2, 18], [:work, 3, 8], [:tennis, 3, 17], [:home, 3, 20], [:work, 4, 8], [:home, 4, 18], [:work, 5, 8], [:home, 5, 18],

76 features scaling XX.each do destination, day, time yy << LOCATIONS_INDEXED[destination] xx << [day.to_f/7, time.to_f/24] end

77 one hidden layer with 25 units

78 100% accuracy on training set

79 [ [1, 16.5], [1, 17], [1, 17.5], [1, 17.8], [2, 17], [2, 18.1], [4, 18], [6, 23], [7, 13], ].each do day, time res = nn.predict_with_probabilities([ [day.to_f/7, time.to_f/24] ]).first. select { v v[0] > 0} # filter zero probabilities puts "#{day} #{time} \t #{res.map { v [LOCATIONS[v[1]], v[0]]}.inspect}" end

80 [[:tennis, 0.97]] 1 17 [[:tennis, 0.86], [:home, 0.06]] [[:home, 0.52], [:tennis, 0.49]] [[:home, 0.82], [:tennis, 0.22]] 2 17 [[:tennis, 0.85], [:home, 0.06]] [[:home, 0.95], [:tennis, 0.07]] 4 18 [[:home, 0.96], [:tennis, 0.08]] 6 23 [[:home, 1.00]] [:work, 1, 8], [:tennis, 1, 17], [:home, 1, 20], [:work, 2, 8], [:home, 2, 18], [:work, 3, 8], [:tennis, 3, 17], [:home, 3, 20], [:work, 4, 8], [:home, 4, 18], [:work, 5, 8], [:home, 5, 18], [:parents, 7, 13], [:home, 7, 18], # week 2 [:work, 1, 8], [:home, 1, 18], [:work, 2, 8], [:home, 2, 18], [:work, 3, 8], [:tennis, 3, 17], [:home, 3, 20], [:work, 4, 8], [:home, 4, 18], [:work, 5, 8], [:home, 5, 18],

81 borisnadion/suggested-destination-demo ruby code of the demo

82 tensorflow but you will need to learn Python

83 clustering unsupervised learning

84 {X (i) } no labels

85

86 anomaly detection unsupervised learning

87

88 collaborative filtering unsupervised learning

89 Jane Arthur John Star Wars VII Dr. Strange 5 5? Arrival 5? 1

90 automatic features and their weights detection based on the user votes

91 similarity between users and between items

92 what to google

93

94 thanks! Boris Nadion