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01-Chapter 1 What is deep learning.mp4
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76.8 MB
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1
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240 KB
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02-Chapter 1 Learning rules and representations from data.mp4
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83.4 MB
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2
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03-Chapter 1 Understanding how deep learning works, in three figures.mp4
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102.9 MB
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3
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878.9 KB
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04-Chapter 1 Before deep learning - A brief history of machine learning.mp4
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82.7 MB
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5
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353.9 KB
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05-Chapter 1 Back to neural networks.mp4
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06-Chapter 1 Why deep learning Why now.mp4
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56.5 MB
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6
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07-Chapter 1 Algorithms.mp4
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62.4 MB
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7
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08-Chapter 2 The mathematical building blocks of neural networks.mp4
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52 MB
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8
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67.4 KB
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09-Chapter 2 Data representations for neural networks.mp4
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47.6 MB
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9
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430 KB
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10-Chapter 2 Real-world examples of data tensors.mp4
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48.6 MB
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10
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520.3 KB
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11-Chapter 2 The gears of neural networks - Tensor operations.mp4
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46.5 MB
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11
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180.4 KB
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12-Chapter 2 Tensor reshaping.mp4
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36.7 MB
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12
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403.5 KB
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13
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804.4 KB
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13-Chapter 2 The engine of neural networks - Gradient-based optimization.mp4
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50 MB
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14
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71.9 KB
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14-Chapter 2 Derivative of a tensor operation - The gradient.mp4
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69.5 MB
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15
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213 KB
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15-Chapter 2 Chaining derivatives - The Backpropagation algorithm.mp4
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55 MB
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16
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51.6 KB
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16-Chapter 2 Looking back at our first example.mp4
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53.7 MB
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17-Chapter 3 Introduction to Keras and TensorFlow.mp4
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67.6 MB
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17
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18-Chapter 3 Setting up a deep learning workspace.mp4
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43.5 MB
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18
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623.7 KB
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19
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713 KB
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19-Chapter 3 First steps with TensorFlow.mp4
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69.2 MB
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20
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470.2 KB
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20-Chapter 3 Anatomy of a neural network - Understanding core Keras APIs.mp4
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56.1 MB
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21-Chapter 3 The “compile” step - Configuring the learning process.mp4
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68 MB
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21
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483.8 KB
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22
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291.1 KB
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22-Chapter 4 Getting started with neural networks - Classification and regression.mp4
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53.4 MB
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23
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469.3 KB
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23-Chapter 4 Building your model.mp4
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64.2 MB
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24
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135.5 KB
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24-Chapter 4 Classifying newswires - A multiclass classification example.mp4
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56.8 MB
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25-Chapter 4 Predicting house prices - A regression example.mp4
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61.8 MB
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25
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497 KB
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26
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547.9 KB
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26-Chapter 5 Fundamentals of machine learning.mp4
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57 MB
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27
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657.3 KB
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27-Chapter 5 The nature of generalization in deep learning.mp4
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80.5 MB
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28
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857.2 KB
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28-Chapter 5 Evaluating machine learning models.mp4
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72.5 MB
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29-Chapter 5 Improving model fit.mp4
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40.5 MB
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30-Chapter 5 Improving generalization.mp4
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69.4 MB
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30
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107.1 KB
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31
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425.2 KB
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31-Chapter 5 Regularizing your model.mp4
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60.8 MB
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32-Chapter 6 The universal workflow of machine learning.mp4
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66.9 MB
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32
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33-Chapter 6 Collect a dataset.mp4
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85.1 MB
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33
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34-Chapter 6 Develop a model.mp4
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44.4 MB
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34
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22.8 KB
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35
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908 KB
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35-Chapter 6 Beat a baseline.mp4
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41.2 MB
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36-Chapter 6 Deploy the model.mp4
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78.2 MB
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36
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113.4 KB
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37
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680.1 KB
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37-Chapter 6 Monitor your model in the wild.mp4
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35.1 MB
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38
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830 KB
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38-Chapter 7 Working with Keras - A deep dive.mp4
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69.9 MB
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39
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442.4 KB
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39-Chapter 7 Subclassing the Model class.mp4
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35.1 MB
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40-Chapter 7 Using built-in training and evaluation loops.mp4
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60.2 MB
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40
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19.6 KB
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41
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505.6 KB
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41-Chapter 7 Writing your own training and evaluation loops.mp4
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45.8 MB
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42-Chapter 7 Make it fast with tf.function.mp4
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36 MB
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42
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660.8 KB
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43
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174.2 KB
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43-Chapter 8 Introduction to deep learning for computer vision.mp4
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40.7 MB
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44-Chapter 8 The convolution operation.mp4
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74.4 MB
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44
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344.7 KB
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45-Chapter 8 Training a convnet from scratch on a small dataset.mp4
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67.1 MB
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45
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225.7 KB
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46-Chapter 8 Data preprocessing.mp4
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61.7 MB
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46
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741.6 KB
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47
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850.9 KB
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47-Chapter 8 Leveraging a pretrained model.mp4
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65.1 MB
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48-Chapter 8 Feature extraction with a pretrained model.mp4
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64.3 MB
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48
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93.1 KB
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49-Chapter 9 Advanced deep learning for computer vision.mp4
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99.8 MB
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49
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153.8 KB
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50-Chapter 9 Modern convnet architecture patterns.mp4
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58.7 MB
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50
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90 KB
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51
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264.1 KB
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51-Chapter 9 Residual connections.mp4
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57.3 MB
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52-Chapter 9 Depthwise separable convolutions.mp4
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67.9 MB
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52
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494.9 KB
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53-Chapter 9 Interpreting what convnets learn.mp4
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58.5 MB
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53
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553.3 KB
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54
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117.9 KB
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54-Chapter 9 Visualizing convnet filters.mp4
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40.4 MB
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55-Chapter 9 Visualizing heatmaps of class activation.mp4
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74.3 MB
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55
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640.7 KB
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56
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665.9 KB
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56-Chapter 10 Deep learning for timeseries.mp4
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53.9 MB
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57
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41.9 KB
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57-Chapter 10 Preparing the data.mp4
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47 MB
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58
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179.1 KB
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58-Chapter 10 Let’s try a basic machine learning model.mp4
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45 MB
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59
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544 KB
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59-Chapter 10 Understanding recurrent neural networks.mp4
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40.5 MB
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60-Chapter 10 A recurrent layer in Keras.mp4
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41.7 MB
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60
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883 KB
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61-Chapter 10 Advanced use of recurrent neural networks.mp4
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59.8 MB
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61
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919.7 KB
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62-Chapter 10 Using bidirectional RNNs.mp4
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64.9 MB
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62
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199.3 KB
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63-Chapter 11 Deep learning for text.mp4
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57.9 MB
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64
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110.2 KB
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64-Chapter 11 Preparing text data.mp4
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46.5 MB
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65
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307.4 KB
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65-Chapter 11 Vocabulary indexing.mp4
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50.4 MB
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66
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395.3 KB
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66-Chapter 11 Two approaches for representing groups of words - Sets and sequences.mp4
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79.8 MB
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67-Chapter 11 Processing words as a sequence - The sequence model approach, Part 1.mp4
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70.5 MB
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67
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517.8 KB
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68
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589 KB
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68-Chapter 11 Processing words as a sequence - The sequence model approach, Part 2.mp4
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52.5 MB
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69
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466.6 KB
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69-Chapter 11 The Transformer architecture.mp4
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71.7 MB
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70-Chapter 11 The Transformer encoder.mp4
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72.5 MB
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70
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991.6 KB
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71
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657.8 KB
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71-Chapter 11 Beyond text classification - Sequence-to-sequence learning.mp4
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79.6 MB
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72
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40.1 KB
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72-Chapter 11 Sequence-to-sequence learning with Transformer.mp4
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56.1 MB
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73
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436 KB
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73-Chapter 12 Generative deep learning.mp4
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80.6 MB
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74-Chapter 12 How do you generate sequence data.mp4
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81.9 MB
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74
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117 KB
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75-Chapter 12 A text-generation callback with variable-temperature sampling.mp4
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58.5 MB
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75
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445.3 KB
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76
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24.2 KB
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76-Chapter 12 DeepDream.mp4
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57.4 MB
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77
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512.4 KB
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77-Chapter 12 Neural style transfer.mp4
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80.9 MB
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78-Chapter 12 Generating images with variational autoencoders.mp4
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55.8 MB
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78
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528.9 KB
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79
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208.6 KB
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79-Chapter 12 Implementing a VAE with Keras.mp4
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75.9 MB
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80-Chapter 12 A bag of tricks.mp4
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62.5 MB
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80
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688.6 KB
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81
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977.5 KB
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81-Chapter 13 Best practices for the real world.mp4
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63 MB
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82
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624.9 KB
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82-Chapter 13 Hyperparameter optimization.mp4
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75.1 MB
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83-Chapter 13 Scaling-up model training.mp4
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53.6 MB
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83
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473.3 KB
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84-Chapter 13 Multi-GPU training.mp4
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37.5 MB
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84
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343.3 KB
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85-Chapter 13 TPU training.mp4
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41.6 MB
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85
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429.1 KB
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86
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866.4 KB
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86-Chapter 14 Conclusions.mp4
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81.2 MB
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87
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355.8 KB
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87-Chapter 14 Key enabling technologies.mp4
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63.6 MB
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88-Chapter 14 Key network architectures.mp4
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59.9 MB
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88
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508.3 KB
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89
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556.4 KB
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89-Chapter 14 The limitations of deep learning.mp4
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60.3 MB
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90
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606.4 KB
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90-Chapter 14 Local generalization vs. extreme generalization.mp4
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45.3 MB
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91
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468.3 KB
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91-Chapter 14 The purpose of intelligence.mp4
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53.5 MB
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92-Chapter 14 Setting the course toward greater generality in AI.mp4
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69.5 MB
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92
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290 KB
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93-Chapter 14 Implementing intelligence - The missing ingredients.mp4
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66 MB
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93
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11.4 KB
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94-Chapter 14 The missing half of the picture.mp4
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47.9 MB
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95-Chapter 14 Blending together deep learning and program synthesis.mp4
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58.9 MB
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96-Chapter 14 Lifelong learning and modular subroutine reuse.mp4
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84.1 MB
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TutsNode.com.txt
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TXT
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102.4 B
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[TGx]Downloaded from torrentgalaxy.to .txt
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614.4 B
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