Deep Learning Patterns and Practices, Video Edition
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13:53:11
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1 - Part 1. Deep learning fundamentals.mp4
01:02
2 - Chapter 1 Designing modern machine learning.mp4
12:43
3 - Chapter 1 The evolution in machine learning approaches.mp4
07:41
4 - Chapter 1 Next steps in computer learning - Part 1.mp4
09:57
5 - Chapter 1 Next steps in computer learning - Part 2.mp4
08:21
6 - Chapter 1 The benefits of design patterns.mp4
06:48
7 - Chapter 2 Deep neural networks.mp4
11:11
8 - Chapter 2 Sequential API method.mp4
08:22
9 - Chapter 2 Activation functions.mp4
12:15
10 - Chapter 2 DNN binary classifier.mp4
12:39
11 - Chapter 2 Simple image classifier.mp4
11:07
12 - Chapter 3 Convolutional and residual neural networks.mp4
08:21
13 - Chapter 3 Feature detection.mp4
07:38
14 - Chapter 3 The ConvNet design for a CNN.mp4
09:34
15 - Chapter 3 VGG networks.mp4
06:45
16 - Chapter 3 Architecture.mp4
09:41
17 - Chapter 3 Batch normalization.mp4
06:23
18 - Chapter 4 Training fundamentals.mp4
08:28
19 - Chapter 4 Dataset splitting.mp4
08:07
20 - Chapter 4 Data normalization.mp4
06:09
21 - Chapter 4 Validation and overfitting.mp4
08:15
22 - Chapter 4 Convergence.mp4
10:16
23 - Chapter 4 Hyperparameters.mp4
09:07
24 - Chapter 4 Learning rate.mp4
06:27
25 - Chapter 4 Invariance.mp4
10:01
26 - Chapter 4 Scale invariance.mp4
05:51
27 - Chapter 4 Raw (disk) datasets.mp4
08:00
28 - Chapter 4 Resizing.mp4
06:42
29 - Part 2. Basic design pattern.mp4
02:04
30 - Chapter 5 Procedural design pattern.mp4
08:10
31 - Chapter 5 Stem component.mp4
10:02
32 - Chapter 5 ResNet.mp4
08:02
33 - Chapter 5 Pre-stem.mp4
11:31
34 - Chapter 5 Task component.mp4
11:10
35 - Chapter 5 Beyond computer vision - NLP.mp4
08:39
36 - Chapter 6 Wide convolutional neural networks.mp4
10:39
37 - Chapter 6 Inception v1 module.mp4
11:43
38 - Chapter 6 Inception v2 - Factoring convolutions.mp4
10:37
39 - Chapter 6 Normal convolution.mp4
07:41
40 - Chapter 6 ResNeXt - Wide residual neural networks.mp4
12:06
41 - Chapter 6 Beyond computer vision - Structured data.mp4
08:05
42 - Chapter 7 Alternative connectivity patterns.mp4
11:44
43 - Chapter 7 Dense block.mp4
06:55
44 - Chapter 7 Xception - Extreme Inception.mp4
10:09
45 - Chapter 7 Exit flow of Xception.mp4
07:23
46 - Chapter 7 SE-Net - Squeeze and excitation.mp4
07:07
47 - Chapter 8 Mobile convolutional neural networks.mp4
11:39
48 - Chapter 8 Stem.mp4
10:01
49 - Chapter 8 MobileNet v2.mp4
12:05
50 - Chapter 8 SqueezeNet.mp4
10:42
51 - Chapter 8 Classifier.mp4
09:05
52 - Chapter 8 ShuffleNet v1.mp4
08:57
53 - Chapter 8 Learner.mp4
06:16
54 - Chapter 8 Deployment.mp4
08:22
55 - Chapter 9 Autoencoders.mp4
11:23
56 - Chapter 9 Convolutional autoencoders.mp4
09:16
57 - Chapter 9 Super-resolution.mp4
12:18
58 - Chapter 9 Pretext tasks.mp4
09:04
59 - Part 3. Working with pipelines.mp4
02:18
60 - Chapter 10 Hyperparameter tuning.mp4
07:58
61 - Chapter 10 Lottery hypothesis.mp4
07:20
62 - Chapter 10 Hyperparameter search fundamentals.mp4
09:06
63 - Chapter 10 Random search.mp4
09:25
64 - Chapter 10 Learning rate scheduler.mp4
12:18
65 - Chapter 10 Regularization.mp4
09:28
66 - Chapter 11 Transfer learning.mp4
11:45
67 - Chapter 11 New classifier.mp4
10:21
68 - Chapter 11 TF Hub prebuilt models.mp4
11:17
69 - Chapter 11 Distinct tasks.mp4
12:23
70 - Chapter 12 Data distributions.mp4
11:21
71 - Chapter 12 Out of distribution.mp4
11:48
72 - Chapter 12 Training as a CNN.mp4
12:06
73 - Chapter 13 Data pipeline.mp4
05:28
74 - Chapter 13 Compressed and raw-image formats.mp4
09:14
75 - Chapter 13 HDF5 format.mp4
10:56
76 - Chapter 13 TFRecord format.mp4
11:33
77 - Chapter 13 Data feeding.mp4
09:45
78 - Chapter 13 Data preprocessing.mp4
12:28
79 - Chapter 13 Preprocessing with TF Extended.mp4
07:31
80 - Chapter 13 Data augmentation.mp4
11:25
81 - Chapter 14 Training and deployment pipeline.mp4
12:21
82 - Chapter 14 Model feeding with tf.data.Dataset.mp4
11:33
83 - Chapter 14 Model feeding with TFX.mp4
11:43
84 - Chapter 14 Training schedulers.mp4
12:53
85 - Chapter 14 Model evaluations.mp4
09:24
86 - Chapter 14 TFX evaluation.mp4
06:15
87 - Chapter 14 Serving predictions.mp4
11:45
88 - Chapter 14 TFX pipeline components for deployment.mp4
12:11
89 - Chapter 14 Evolution in production pipeline design.mp4
07:06
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- language english
- Training sessions 89
- duration 13:53:11
- Release Date 2023/11/06
