Artificial Intelligence Masterclass
67 videos • 234 views • by Tutorials Collection
1
1. Introduction + Course Structure + Demo
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2
2. Your Three Best Resources
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3
3. Plan of Attack
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4
4. The Neuron
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5
5. The Activation Function
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6
6. How do Neural Networks work
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7
7. How do Neural Networks learn
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8
8. Gradient Descent
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9
9. Backpropagation
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10
10. Plan of Attack
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11
11. What are Convolutional Neural Networks
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12
12. Step 1 - The Convolution Operation
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13
13. Step 1 Bis - The ReLU Layer
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14
14. Step 2 - Pooling
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15
15. Step 3 - Flattening
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16
16. Step 4 - Full Connection
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17
17. Summary
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18
18. Softmax & Cross Entropy
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19
19. Plan of Attack
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20
20. What are AutoEncoders
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21
21. A Note on Biases
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22
22. Training an AutoEncoder
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23
23, Overcomplete Hidden Layers
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24
24. Sparse AutoEncoders
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25
25. Denoising AutoEncoders
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26
26. Contractive AutoEncoders
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27
27. Stacked AutoEncoders
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28
28. Deep AutoEncoders
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29
29. Introduction to the VAE
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30
30. Reparameterization Trick
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31
31. Variational AutoEncoders
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32
32. Introduction to Step 5
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33
33. Initializing all the parameters and variables of the CNN VAE class
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34
34. Building the Encoder part of the VAE
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35
35. Building the V part of the VAE
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36
36. Building the Decoder part of the VAE
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37
37. Implementing the Training operations
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38
38. Plan of Attack
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39
39. What are Recurrent Neural Networks
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40
40. The Vanishing Gradient Problem
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41
41. LSTMs
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42
42. LSTM Practical Intuition
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43
43. LSTM Variations
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44
44. Introduction to the MDN RNN
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45
45. Mixture Density Networks
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46
46. VAE + MDN RNN Visualization
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47
47. Initializing all the parameters and variables of the MDN RNN class
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48
48. Building the RNN Gathering the parameters
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49
49. Building the RNN - Creating an LSTM cell with Dropout
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50
50. Building the RNN - Setting up the Input, Target, and Output of the RNN
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51
51. Building the RNN - Getting the Deterministic Output of the RNN
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52
52. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN
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53
53. Building the MDN - Getting the MDN parameters
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54
54. Implementing the Training operations Part 1
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55
55. Implementing the Training operations Part 2
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56
56. What is Reinforcement Learning?
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57
57. A Pseudo Implementation of Reinforcement Learning for the Full World Model
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58
58. Deep NeuroEvolution
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59
59. Evolution Strategies
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60
60. Genetic Algorithms
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61
61. Covariance Matrix Adaptation Evolution Strategy CMA ES
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62
62. Parameter Exploring Policy Gradients PEPG
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63
63. OpenAI Evolution Strategy
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64
64. The Whole Implementation
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65
65. Installing the required packages
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66
66. The Final Race Human Intelligence vs Artificial Intelligence
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67
67. THANK YOU! Bonus video
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