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![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],](https://image.slidesharecdn.com/machine-learning-161208065538/75/Machine-Learning-Make-Your-Ruby-Code-Smarter-75-2048.jpg)
![XX.each do |destination, day, time| yy << LOCATIONS_INDEXED[destination] xx << [day.to_f/7, time.to_f/24] end features scaling](https://image.slidesharecdn.com/machine-learning-161208065538/75/Machine-Learning-Make-Your-Ruby-Code-Smarter-76-2048.jpg)
![[ [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](https://image.slidesharecdn.com/machine-learning-161208065538/75/Machine-Learning-Make-Your-Ruby-Code-Smarter-79-2048.jpg)
![1 16.5 [[:tennis , 0.97]] 1 17 [[:tennis , 0.86], [:home , 0.06]] 1 17.5 [[:home , 0.52], [:tennis, 0.49]] 1 17.8 [[:home , 0.82], [:tennis, 0.22]] 2 17 [[:tennis , 0.85], [:home , 0.06]] 2 18.1 [[: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],](https://image.slidesharecdn.com/machine-learning-161208065538/75/Machine-Learning-Make-Your-Ruby-Code-Smarter-80-2048.jpg)
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Machine Learning: Make Your Ruby Code Smarter
The document discusses concepts in machine learning (ML) and artificial intelligence (AI), explaining the differences between supervised and unsupervised learning, and detailing linear regression and logistic regression. It also covers neural networks, deep learning, and specific applications like recommendation systems and clustering. Additionally, it provides code examples, feature scaling techniques, and resources for further learning.
Machine Learning: Make Your Ruby Code Smarter
- 1.
- 2.
- 3.
- 4. awesome web andmobile apps since 2005
- 11.
- 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 explicitlyprogrammed
- 15. FF NN costfunction
- 16. FF NN CostFunction I’m kidding
- 17. cost function withregularization
- 18. 2 types ofML supervised learning unsupervised learning
- 19. supervised the training datais labeled, eg. we know the correct answer
- 20. unsupervised the training datais not labeled, eg. we would figure out hidden correlations by ourselves
- 21.
- 22. (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) y(i)
- 23. training set learning algorithm hθ(x)x(newdata) y(prediction)
- 24. training set learning algorithm hθ(x)x(newdata) y(prediction)
- 25.
- 26. (x, y) y =hθ(x) = θ0 + θ1x find θ0 and θ1
- 27. hθ(x) = θ0+ θ1x1 + θ2x2 + …+ θnxn many features, n - number of features
- 28. size, sq.m x1 # rooms x2 age x3 price y 803 22 2.9M 90 4 24 3.1M 75 3 28 2.5M 110 5 20 3.3M
- 29. 1 USD =3.85 NIS
- 30. hθ(x) = θ0+ θ1x1 summate the prediction error on training set
- 31. Linear Regression CostFunction
- 32. minimize J(θ) funding aminimum 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 θ0, θ1, θ2, …, θn magic inside
- 35. hθ(x) = θ0+ θ1x1 + θ2x2 + …+ θnxn we’re ready to predict
- 36. features scaling 0 ≤x ≤ 1
- 37. size, sq.m size, sq.m/ 110 x1 80 0.72 90 0.81 75 0.68 110 1
- 38. mean normalization average valueof the feature is ~0 -0.5 ≤ x ≤ 0.5
- 39. size, sq.m (size, sq.m/ 110) - 0.8025 x1 80 -0.0825 90 0.075 75 -0.1226 110 0.1975
- 40. matrix manipulations X =n x 1 vector, ϴ = n x 1 vector hθ(x) = θ0 + θ1x1 + θ2x2 + …+ θnxn hθ(x) = ϴT X
- 41.
- 43.
- 44.
- 45. y = 1,true y = 0, false
- 46. hθ(x) = g(ϴT X) hθ(X)- estimated probability that y = 1 on input X g(z) - logistic non-linear function
- 47. logistic function g(z) thereis a few: sigmoid, tahn, ReLUs, etc image source: Wikipedia
- 48. (x(1) , y(1) ), (x(2) ,y(2) )…(x(m) , y(m) ) minimize the cost function vector θ y = {0, 1}
- 49. training set learning algorithm hθ(x)x(newdata) y(prediction) hθ(x) = g(ϴT X) y ≥ 0.5 - true y < 0.5 - false
- 50.
- 52. y = 1,true y = 0, false y = 0, false
- 53. don’t implement itat home use libsvm, liblinear, and others
- 54.
- 55.
- 56. feed forward neuralnetwork input layer hidden layer output layer
- 57.
- 58.
- 59. logistic unit x0 x1 x2 θ1 θ2 θ3 hθ= g(x0θ0 + x1θ1 + x2θ2) θ - weights g - activation function
- 60. logistic function g(z) thereis a few: sigmoid, tahn, ReLUs, etc image source: Wikipedia
- 61. output: probabilities 0.4765 thaty = 2 0.7123 that y = 1
- 62. net with nohidden layers no hidden layers = one-vs-all logistic regression
- 63. cost function sometimes calledloss function of NN, a representation of an error between a real and a predicted value
- 64. training set learning algorithm θx(newdata) y(prediction)
- 65.
- 66. gradient descent +backprop “deep learning” - is training a neural net “deep” - because we have many layers
- 67. convolutional neural nets widelyused for image processing and object recognition
- 68. recurrent neural nets widelyused for natural language processing
- 69.
- 70.
- 71.
- 72.
- 73.
- 74. tangledpath/ruby-fann Ruby library forinterfacing 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. XX.each do |destination,day, time| yy << LOCATIONS_INDEXED[destination] xx << [day.to_f/7, time.to_f/24] end features scaling
- 77. 2 ➞ 25➞ 4 one hidden layer with 25 units
- 78.
- 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. 1 16.5 [[:tennis, 0.97]] 1 17 [[:tennis , 0.86], [:home , 0.06]] 1 17.5 [[:home , 0.52], [:tennis, 0.49]] 1 17.8 [[:home , 0.82], [:tennis, 0.22]] 2 17 [[:tennis , 0.85], [:home , 0.06]] 2 18.1 [[: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.
- 82. tensorflow but you willneed to learn Python
- 83.
- 84.
- 86.
- 88.
- 89. Jane Arthur John StarWars VII 5 5 1 Dr. Strange 5 5 ? Arrival 5 ? 1
- 90. automatic features and theirweights detection based on the user votes
- 91. similarity between users andbetween items
- 92.
- 93.
- 94.