Addison Wesley Professional Machine Vision GANs and Deep Reinforcement Learning Sneak

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6:05:32

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01 - Machine Vision, GANs, Deep Reinforcement Learning - Introduction.mp4

02:47

02 - Topics.mp4

00:29

03 - 1.1 Running the Hands-On Code Examples in Jupyter Notebooks.mp4

13:19

04 - 1.2 Review of Prerequisite Deep Learning Theory.mp4

14:51

05 - 1.3 A Sneak Peak.mp4

06:23

06 - Topics.mp4

00:28

07 - 2.1 Convolutional Layers.mp4

27:48

08 - 2.2 Convolutional Filter Hyperparameters.mp4

07:05

09 - 2.3 Activation Pooling and Flattening.mp4

05:32

10 - 2.4 Building A ConvNet in TensorFlow.mp4

19:33

11 - 2.5 ConvNet Model Architectures.mp4

10:20

12 - 2.6 Residual Networks.mp4

09:51

13 - 2.7 Image Segmentation.mp4

16:07

14 - 2.8 Object Detection.mp4

07:34

15 - 2.9 Transfer Learning.mp4

16:16

16 - 2.10 Capsule Networks.mp4

01:20

17 - Topics.mp4

00:44

18 - 3.1 A Boozy All-Nighter.mp4

04:23

19 - 3.2 Latent Space - Arithmetic on Fake Human Faces.mp4

05:35

20 - 3.3 Style Transfer - Converting Photos into Monet (and Vice Versa).mp4

03:31

21 - 3.4 Applications of GANs.mp4

02:42

22 - 3.5 Essential GAN Theory.mp4

10:03

23 - 3.6 The Quick, Draw! Dataset.mp4

12:53

24 - 3.7 The Discriminator Network.mp4

11:28

25 - 3.8 The Generator Network.mp4

10:39

26 - 3.9 Training the Adversarial Network.mp4

19:34

27 - Topics.mp4

00:23

28 - 4.1 Three Categories of Machine Learning Problems.mp4

07:17

29 - 4.2 When Reinforcement Learning Becomes Deep.mp4

02:46

30 - 4.3 Applications to Video Games.mp4

04:03

31 - 4.4 Applications to Board Games.mp4

13:15

32 - 4.5 Real-World Applications.mp4

02:32

33 - 4.6 Reinforcement Learning Environments.mp4

03:51

34 - 4.7 Three Categories of Artificial Intelligence.mp4

03:57

35 - Topics.mp4

01:03

36 - 5.1 The Cart-Pole Game.mp4

03:05

37 - 5.2 Essential Reinforcement Learning Theory.mp4

12:14

38 - 5.3 Deep Q-Learning Networks.mp4

10:16

39 - 5.4 Defining a DQN Agent.mp4

24:12

40 - 5.5 Interacting with an Environment.mp4

07:54

41 - 5.6 Hyperparameter Optimization with SLM Lab.mp4

05:47

42 - 5.7 Agents Beyond DQN.mp4

05:49

43 - 5.8 Datasets, Project Ideas, and Resources for Self-Study.mp4

08:46

44 - 5.9 Approaching Artificial General Intelligence.mp4

05:57

45 - Machine Vision, GANs, Deep Reinforcement Learning - Summary.mp4

01:10

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