4shared - G.DL.in.M by vijay

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08:54

01 - Introduction.mp4

08:54

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05:28

02 - What you need to get started.mp4

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05:01

03 - What is Deep Learning and Machine Learning.mp4

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05:22

04 - Supervised vs. unsupervised learning.mp4

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12:56

05 - Parametric vs. non-parametric learning.mp4

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07:55

06 - Making a prediction.mp4

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04:05

07 - What does a Neural Network do.mp4

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13:24

08 - Multiple inputs.mp4

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09:14

09 - Multiple outputs and stacking predictions.mp4

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11:26

10 - Primer on NumPy.mp4

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06:23

11 - Compare and learn.mp4

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09:16

13 - Hot and cold learning.mp4

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09:20

14 - Gradient descent.mp4

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09:05

15 - Learning with gradient decent.mp4

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07:12

16 - The secret to learning.mp4

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11:40

17 - How to use a derivative to learn.mp4

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06:12

18 - Alpha.mp4

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07:16

19 - Gradient descent learning with multiple inputs.mp4

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06:04

20 - Several steps of learning.mp4

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06:16

21 - Gradient descent with multiple outputs.mp4

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09:32

22 - Visualizing weight values.mp4

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10:31

23 - The streetlight problem.mp4

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09:37

24 - Building our neural network.mp4

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14:41

25 - Up and down pressure.mp4

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08:04

26 - Correlation and backpropagation.mp4

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08:06

27 - Linear vs. non-linear.mp4

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10:14

28 - Our first 'deep' neural network.mp4

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06:35

29 - Simplifying.mp4

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07:16

30 - Simplified visualization.mp4

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08:04

31 - Seeing the network predict.mp4

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