An AI, NEAT plus ultra

You've been hearing about AI for a while, but it's still obscure for you.
How does it work, what's behind this word?
If reading white papers or doctoral papers is not your thing, the tensorflow doc is just a big pile of words, and if you've seen unclear presentations using the same vocabulary without really give you an idea of how it works and how to implement an AI at home... This presentation is for you.
At its end, you will be able to play with an AI, simple, but that will serve as a gateway to the beautiful world of machine-learning.

Transcript

1. UNE IA
 NEAT PLUS ULTRA
2. UNE IA
 NEAT PLUS ULTRA
3. Grégoire Hébert Senior Developper - Trainer @ Les-Tilleuls.coop @gheb_dev @gregoirehebert UNE IA
 NEAT PLUS ULTRA
4. @gheb_dev @gregoirehebert
5. @gheb_dev @gregoirehebert REACTIVE MACHINES (Senarii reactive)
6. @gheb_dev @gregoirehebert REACTIVE MACHINES (Senarii reactive) LIMITED MEMORY (Environment reactive)
7. @gheb_dev @gregoirehebert REACTIVE MACHINES (Senarii reactive) LIMITED MEMORY (Environment reactive) THEORY OF MIND (People awareness)
8. @gheb_dev @gregoirehebert REACTIVE MACHINES (Senarii reactive) LIMITED MEMORY (Environment reactive) THEORY OF MIND (People awareness) SELF AWARE
9. SELF AWARE @gheb_dev @gregoirehebert REACTIVE MACHINES (Senarii reactive) LIMITED MEMORY (Environment reactive) THEORY OF MIND (People awareness)
10. @gheb_dev @gregoirehebert REACTIVE MACHINES (Senarii reactive)
11. @gheb_dev @gregoirehebert INPUT
12. @gheb_dev @gregoirehebert INPUT ?
13. @gheb_dev @gregoirehebert INPUT ? OUTPUT
14. @gheb_dev @gregoirehebert INPUT ? OUTPUT PERCEPTRON
15. @gheb_dev @gregoirehebert ?
16. @gheb_dev @gregoirehebert ? Or not
17. @gheb_dev @gregoirehebert ? Or not 0 - 10
18. @gheb_dev @gregoirehebert ? Or not 0 - 10 0 - 1 0 - 1 Activation Activation
19. ? Or not 0 - 10 0 - 1 0 - 1 Activation Activation @gheb_dev @gregoirehebert
20. @gheb_dev @gregoirehebert 0 - 10 ? Or not 0 - 1 0 - 1 Activation Activation
21. @gheb_dev @gregoirehebert
22. @gheb_dev @gregoirehebert Binary Step
23. @gheb_dev @gregoirehebert Binary Step Gaussian
24. @gheb_dev @gregoirehebert Binary Step Gaussian Hyperbolic Tangent
25. @gheb_dev @gregoirehebert Binary Step Gaussian Hyperbolic Tangent Parametric Rectified Linear Unit
26. @gheb_dev @gregoirehebert Binary Step Gaussian Hyperbolic Tangent Parametric Rectified Linear Unit Sigmoid
27. @gheb_dev @gregoirehebert Binary Step Gaussian Hyperbolic Tangent Parametric Rectified Linear Unit Sigmoid Thresholded Rectified Linear Unit
28. @gheb_dev @gregoirehebert Binary Step Gaussian Hyperbolic Tangent Parametric Rectified Linear Unit Sigmoid Thresholded Rectified Linear Unit
29. @gheb_dev @gregoirehebert Sigmoid
30. 0 - 10 ? 0 - 1 0 - 1 Activation Activation
31. @gheb_dev @gregoirehebert ? Or not 0 - 10 0 - 1 0 - 1 Sigmoid Sigmoid
32. ? Or not 0 - 10 0 - 1 0 - 1 Sigmoid Sigmoid @gheb_dev @gregoirehebert
33. ? Or not 0 - 10 0 - 1 0 - 1 Sigmoid Sigmoid @gheb_dev @gregoirehebert Bias Bias
34. ? Or not 8 0.2 0.3 Sigmoid Sigmoid @gheb_dev @gregoirehebert 0.4 0.8
35. H Or not 8 0.2 0.3 Sigmoid Sigmoid @gheb_dev @gregoirehebert 0.4 0.8
36. 0 - 10 ? 0 - 1 0 - 1 Activation Activation
37. @gheb_dev @gregoirehebert H = sigmoid (Input x weight + bias)
38. @gheb_dev @gregoirehebert H = sigmoid (8 x 0.2 + 0.4)
39. @gheb_dev @gregoirehebert H = sigmoid (8 x 0.2 + 0.4) H = 0.88078707797788
40. @gheb_dev @gregoirehebert H = sigmoid (8 x 0.2 + 0.4) H = 0.88078707797788 O = sigmoid (H x w + b)
41. @gheb_dev @gregoirehebert H = sigmoid (8 x 0.2 + 0.4) H = 0.88078707797788 O = sigmoid (H x 0.3 + 0.8)
42. @gheb_dev @gregoirehebert H = sigmoid (8 x 0.2 + 0.4) H = 0.88078707797788 O = sigmoid (H x 0.3 + 0.8) O = 0.74349981350761
43. @gheb_dev @gregoirehebert H = sigmoid (8 x 0.2 + 0.4) H = 0.88078707797788 O = sigmoid (H x 0.3 + 0.8) O = 0.74349981350761
44. @gheb_dev @gregoirehebert H = sigmoid (8 x 0.2 + 0.4) H = 0.88078707797788 O = sigmoid (H x 0.3 + 0.8) O = 0.74349981350761
45. @gheb_dev @gregoirehebert H = sigmoid (2 x 0.2 + 0.4) H = 0.6897448112761 O = sigmoid (H x 0.3 + 0.8) O = 0.73243113381927
46. @gheb_dev @gregoirehebert H = sigmoid (2 x 0.2 + 0.4) H = 0.6897448112761 O = sigmoid (H x 0.3 + 0.8) O = 0.73243113381927
47. H = sigmoid (2 x 0.2 + 0.4) H = 0.6897448112761 O = sigmoid (H x 0.3 + 0.8) O = 0.73243113381927 @gheb_dev @gregoirehebert TRAINING
48. @gheb_dev @gregoirehebert H = sigmoid (2 x 0.2 + 0.4) H = 0.6897448112761 O = sigmoid (H x 0.3 + 0.8) O = 0.73243113381927
49. H Or not 8 0.2 0.3 Sigmoid Sigmoid @gheb_dev @gregoirehebert 0.4 0.8 BACK PROPAGATION
50. H Or not 8 0.2 0.3 Sigmoid Sigmoid @gheb_dev 0.4 0.8 BACK PROPAGATION
51. H Or not 8 0.2 0.3 Sigmoid Sigmoid @gheb_dev 0.4 0.8 BACK PROPAGATION
52. H Or not 8 0.2 0.3 Sigmoid Sigmoid @gheb_dev 0.4 0.8 BACK PROPAGATION LINEAR GRADIENT DESCENT
53. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR
54. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT =
55. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT =
56. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT =
57. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT =
58. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT =
59. @gheb_dev @gregoirehebert LINEAR GRADIENT DESCENT
60. @gheb_dev @gregoirehebert LINEAR GRADIENT DESCENT
61. @gheb_dev @gregoirehebert LINEAR GRADIENT DESCENT
62. @gheb_dev @gregoirehebert LINEAR GRADIENT DESCENT
63. @gheb_dev @gregoirehebert LINEAR GRADIENT DESCENT The derivative or Slope
 
 For any function f, it’s derivative f’
 calculate the direction
 
 S >= 0 then you must increase the value
 S <= 0 then you must decrease the value
64. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT =
65. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT = GRADIENT Sigmoid’ (OUTPUT) =
66. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT = GRADIENT Sigmoid’ (OUTPUT) = Multiplied by the error
67. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT = GRADIENT Sigmoid’ (OUTPUT) = Multiplied by the error And the LEARNING RATE
68. @gheb_dev @gregoirehebert LINEAR GRADIENT DESCENT
69. @gheb_dev @gregoirehebert LINEAR GRADIENT DESCENT
70. @gheb_dev @gregoirehebert LINEAR GRADIENT DESCENT
71. @gheb_dev @gregoirehebert LINEAR GRADIENT DESCENT
72. @gheb_dev @gregoirehebert LINEAR GRADIENT DESCENT
73. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT = GRADIENT Sigmoid’ (OUTPUT) = Multiplied by the error And the LEARNING RATE
74. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT = GRADIENT Sigmoid’ (OUTPUT) = Multiplied by the error And the LEARNING RATE ΔWeights GRADIENT x H =
75. H Or not 8 0.2 0.3 Sigmoid Sigmoid @gheb_dev 0.4 0.8 BACK PROPAGATION LINEAR GRADIENT DESCENT
76. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT = GRADIENT Sigmoid’ (OUTPUT) = Multiplied by the error And the LEARNING RATE ΔWeights GRADIENT x H = Weights ΔWeights + weights =
77. @gheb_dev @gregoirehebert BACK PROPAGATION LINEAR GRADIENT DESCENT ERROR EXPECTATION - OUTPUT = GRADIENT Sigmoid’ (OUTPUT) = Multiplied by the error And the LEARNING RATE ΔWeights GRADIENT x H = Weights ΔWeights + weights = Bias Bias + GRADIENT =
78. H Or not 8 0.2 0.3 Sigmoid Sigmoid @gheb_dev 0.4 0.8 BACK PROPAGATION LINEAR GRADIENT DESCENT
79. H Or not 8 4.80 7.66 Sigmoid Sigmoid @gheb_dev -26.61 -3.75 BACK PROPAGATION LINEAR GRADIENT DESCENT
80. H 8 4.80 7.66 Sigmoid Sigmoid @gheb_dev -26.61 -3.75 BACK PROPAGATION LINEAR GRADIENT DESCENT 0.97988
81. H 4.80 7.66 Sigmoid Sigmoid @gheb_dev -26.61 -3.75 BACK PROPAGATION LINEAR GRADIENT DESCENT 2 0.02295
82. H 4.80 7.66 Sigmoid Sigmoid @gheb_dev -26.61 -3.75 BACK PROPAGATION LINEAR GRADIENT DESCENT 2 0.02295
83. @gheb_dev @gregoirehebert CONGRATULATIONS !
84. CONGRATULATIONS ! Let’s play together :) https://github.com/GregoireHebert/sflive-nn/ @gheb_dev @gregoirehebert
85. CONGRATULATIONS ! Let’s play together :) https://github.com/GregoireHebert/sflive-nn/ @gheb_dev @gregoirehebert
86. @gheb_dev @gregoirehebert Hungry EAT
87. @gheb_dev @gregoirehebert Hungry EAT
88. @gheb_dev @gregoirehebert Hungry EAT MULTI LAYER PERCEPTRON Hungry EAT Hungry EAT
89. @gheb_dev @gregoirehebert Hungry EAT MULTI LAYER PERCEPTRON Thirsty DRINK Sleepy SLEEP
90. @gheb_dev @gregoirehebert Hungry EAT Thirsty DRINK Sleepy SLEEP
91. @gheb_dev @gregoirehebert Hungry EAT Thirsty DRINK Sleepy SLEEP
92. @gheb_dev @gregoirehebert Hungry EAT MULTI LAYER PERCEPTRON Thirsty DRINK Sleepy SLEEP
93. @gheb_dev @gregoirehebert Hungry EAT N.E.A.T. Thirsty DRINK Sleepy SLEEP Neuro Evolution through Augmented Topology
94. @gheb_dev @gregoirehebert Hungry EAT N.E.A.T. Thirsty DRINK Sleepy SLEEP Neuro Evolution through Augmented Topology https://github.com/GregoireHebert/tamagotchi
95. @gheb_dev @gregoirehebert
96. @gheb_dev @gregoirehebert THANK YOU !