Deep Learning with TensorFlow

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6:36:19

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01 - Deep Learning with TensorFlow Introduction.mp4

02:26

02 - Topics.mp4

00:30

03 - 1.1 Neural Networks and Deep Learning.mp4

15:04

04 - 1.2 Running the Code in These LiveLessons.mp4

08:53

05 - 1.3 An Introductory Artificial Neural Network.mp4

29:46

06 - Topics.mp4

00:39

07 - 2.1 The Families of Deep Neural Nets and their Applications.mp4

05:00

08 - 2.2 Essential Theory INeural Units.mp4

23:26

09 - 2.3 Essential Theory IICost Functions, Gradient Descent, and Backpropagation.mp4

15:39

10 - 2.4 TensorFlow PlaygroundVisualizing a Deep Net in Action.mp4

15:31

11 - 2.5 Data Sets for Deep Learning.mp4

06:04

12 - 2.6 Applying Deep Net Theory to Code I.mp4

15:09

13 - Topics.mp4

00:48

14 - 3.1 Essential Theory IIIMini-Batches, Unstable Gradients, and Avoiding Overfitt.mp4

28:22

15 - 3.2 Applying Deep Net Theory to Code II.mp4

21:14

16 - 3.3 Introduction to Convolutional Neural Networks for Visual Recognition.mp4

05:56

17 - 3.4 Classic ConvNet ArchitecturesLeNet-5.mp4

15:42

18 - 3.5 Classic ConvNet ArchitecturesAlexNet and VGGNet.mp4

23:50

19 - 3.6 TensorBoard and the Interpretation of Model Outputs.mp4

13:38

20 - Topics.mp4

00:39

21 - 4.1 Comparison of the Leading Deep Learning Libraries.mp4

04:50

22 - 4.2 Introduction to TensorFlow.mp4

27:55

23 - 4.3 Fitting Models in TensorFlow.mp4

30:09

24 - 4.4 Dense Nets in TensorFlow.mp4

34:13

25 - 4.5 Deep Convolutional Nets in TensorFlow.mp4

28:56

26 - Topics.mp4

00:29

27 - 5.1 Improving Performance and Tuning Hyperparameters.mp4

11:49

28 - 5.2 How to Build Your Own Deep Learning Project.mp4

06:06

29 - 5.3 Resources for Self-Study.mp4

02:21

30 - Deep Learning with TensorFlow Summary.mp4

01:15

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