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| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 0 |
67.25Кб |
| 01-Chapter 1 What is deep learning.mp4 |
76.79Мб |
| 02-Chapter 1 Learning rules and representations from data.mp4 |
83.36Мб |
| 03-Chapter 1 Understanding how deep learning works, in three figures.mp4 |
102.93Мб |
| 04-Chapter 1 Before deep learning - A brief history of machine learning.mp4 |
82.65Мб |
| 05-Chapter 1 Back to neural networks.mp4 |
77.93Мб |
| 06-Chapter 1 Why deep learning Why now.mp4 |
56.47Мб |
| 07-Chapter 1 Algorithms.mp4 |
62.35Мб |
| 08-Chapter 2 The mathematical building blocks of neural networks.mp4 |
52.03Мб |
| 09-Chapter 2 Data representations for neural networks.mp4 |
47.57Мб |
| 1 |
240.01Кб |
| 10 |
520.29Кб |
| 10-Chapter 2 Real-world examples of data tensors.mp4 |
48.57Мб |
| 11 |
180.40Кб |
| 11-Chapter 2 The gears of neural networks - Tensor operations.mp4 |
46.50Мб |
| 12 |
403.53Кб |
| 12-Chapter 2 Tensor reshaping.mp4 |
36.72Мб |
| 13 |
804.37Кб |
| 13-Chapter 2 The engine of neural networks - Gradient-based optimization.mp4 |
49.96Мб |
| 14 |
71.86Кб |
| 14-Chapter 2 Derivative of a tensor operation - The gradient.mp4 |
69.46Мб |
| 15 |
212.96Кб |
| 15-Chapter 2 Chaining derivatives - The Backpropagation algorithm.mp4 |
55.02Мб |
| 16 |
51.61Кб |
| 16-Chapter 2 Looking back at our first example.mp4 |
53.70Мб |
| 17 |
965.69Кб |
| 17-Chapter 3 Introduction to Keras and TensorFlow.mp4 |
67.58Мб |
| 18 |
623.69Кб |
| 18-Chapter 3 Setting up a deep learning workspace.mp4 |
43.54Мб |
| 19 |
712.98Кб |
| 19-Chapter 3 First steps with TensorFlow.mp4 |
69.16Мб |
| 2 |
942.96Кб |
| 20 |
470.20Кб |
| 20-Chapter 3 Anatomy of a neural network - Understanding core Keras APIs.mp4 |
56.10Мб |
| 21 |
483.84Кб |
| 21-Chapter 3 The “compile” step - Configuring the learning process.mp4 |
68.01Мб |
| 22 |
291.13Кб |
| 22-Chapter 4 Getting started with neural networks - Classification and regression.mp4 |
53.42Мб |
| 23 |
469.29Кб |
| 23-Chapter 4 Building your model.mp4 |
64.19Мб |
| 24 |
135.48Кб |
| 24-Chapter 4 Classifying newswires - A multiclass classification example.mp4 |
56.83Мб |
| 25 |
496.95Кб |
| 25-Chapter 4 Predicting house prices - A regression example.mp4 |
61.83Мб |
| 26 |
547.91Кб |
| 26-Chapter 5 Fundamentals of machine learning.mp4 |
56.96Мб |
| 27 |
657.34Кб |
| 27-Chapter 5 The nature of generalization in deep learning.mp4 |
80.49Мб |
| 28 |
857.24Кб |
| 28-Chapter 5 Evaluating machine learning models.mp4 |
72.53Мб |
| 29 |
1012.92Кб |
| 29-Chapter 5 Improving model fit.mp4 |
40.50Мб |
| 3 |
878.90Кб |
| 30 |
107.12Кб |
| 30-Chapter 5 Improving generalization.mp4 |
69.36Мб |
| 31 |
425.25Кб |
| 31-Chapter 5 Regularizing your model.mp4 |
60.78Мб |
| 32 |
933.44Кб |
| 32-Chapter 6 The universal workflow of machine learning.mp4 |
66.92Мб |
| 33 |
84.42Кб |
| 33-Chapter 6 Collect a dataset.mp4 |
85.08Мб |
| 34 |
22.81Кб |
| 34-Chapter 6 Develop a model.mp4 |
44.39Мб |
| 35 |
908.00Кб |
| 35-Chapter 6 Beat a baseline.mp4 |
41.15Мб |
| 36 |
113.35Кб |
| 36-Chapter 6 Deploy the model.mp4 |
78.21Мб |
| 37 |
680.13Кб |
| 37-Chapter 6 Monitor your model in the wild.mp4 |
35.09Мб |
| 38 |
829.96Кб |
| 38-Chapter 7 Working with Keras - A deep dive.mp4 |
69.87Мб |
| 39 |
442.40Кб |
| 39-Chapter 7 Subclassing the Model class.mp4 |
35.13Мб |
| 4 |
659.96Кб |
| 40 |
19.64Кб |
| 40-Chapter 7 Using built-in training and evaluation loops.mp4 |
60.17Мб |
| 41 |
505.60Кб |
| 41-Chapter 7 Writing your own training and evaluation loops.mp4 |
45.80Мб |
| 42 |
660.75Кб |
| 42-Chapter 7 Make it fast with tf.function.mp4 |
35.99Мб |
| 43 |
174.24Кб |
| 43-Chapter 8 Introduction to deep learning for computer vision.mp4 |
40.65Мб |
| 44 |
344.74Кб |
| 44-Chapter 8 The convolution operation.mp4 |
74.39Мб |
| 45 |
225.68Кб |
| 45-Chapter 8 Training a convnet from scratch on a small dataset.mp4 |
67.09Мб |
| 46 |
741.62Кб |
| 46-Chapter 8 Data preprocessing.mp4 |
61.66Мб |
| 47 |
850.94Кб |
| 47-Chapter 8 Leveraging a pretrained model.mp4 |
65.11Мб |
| 48 |
93.12Кб |
| 48-Chapter 8 Feature extraction with a pretrained model.mp4 |
64.34Мб |
| 49 |
153.77Кб |
| 49-Chapter 9 Advanced deep learning for computer vision.mp4 |
99.77Мб |
| 5 |
353.91Кб |
| 50 |
89.95Кб |
| 50-Chapter 9 Modern convnet architecture patterns.mp4 |
58.74Мб |
| 51 |
264.11Кб |
| 51-Chapter 9 Residual connections.mp4 |
57.35Мб |
| 52 |
494.91Кб |
| 52-Chapter 9 Depthwise separable convolutions.mp4 |
67.90Мб |
| 53 |
553.28Кб |
| 53-Chapter 9 Interpreting what convnets learn.mp4 |
58.52Мб |
| 54 |
117.90Кб |
| 54-Chapter 9 Visualizing convnet filters.mp4 |
40.41Мб |
| 55 |
640.69Кб |
| 55-Chapter 9 Visualizing heatmaps of class activation.mp4 |
74.30Мб |
| 56 |
665.93Кб |
| 56-Chapter 10 Deep learning for timeseries.mp4 |
53.89Мб |
| 57 |
41.91Кб |
| 57-Chapter 10 Preparing the data.mp4 |
46.98Мб |
| 58 |
179.07Кб |
| 58-Chapter 10 Let’s try a basic machine learning model.mp4 |
45.05Мб |
| 59 |
544.05Кб |
| 59-Chapter 10 Understanding recurrent neural networks.mp4 |
40.46Мб |
| 6 |
109.55Кб |
| 60 |
883.05Кб |
| 60-Chapter 10 A recurrent layer in Keras.mp4 |
41.66Мб |
| 61 |
919.67Кб |
| 61-Chapter 10 Advanced use of recurrent neural networks.mp4 |
59.85Мб |
| 62 |
199.31Кб |
| 62-Chapter 10 Using bidirectional RNNs.mp4 |
64.89Мб |
| 63 |
1003.89Кб |
| 63-Chapter 11 Deep learning for text.mp4 |
57.88Мб |
| 64 |
110.17Кб |
| 64-Chapter 11 Preparing text data.mp4 |
46.48Мб |
| 65 |
307.35Кб |
| 65-Chapter 11 Vocabulary indexing.mp4 |
50.36Мб |
| 66 |
395.28Кб |
| 66-Chapter 11 Two approaches for representing groups of words - Sets and sequences.mp4 |
79.82Мб |
| 67 |
517.84Кб |
| 67-Chapter 11 Processing words as a sequence - The sequence model approach, Part 1.mp4 |
70.54Мб |
| 68 |
588.99Кб |
| 68-Chapter 11 Processing words as a sequence - The sequence model approach, Part 2.mp4 |
52.54Мб |
| 69 |
466.61Кб |
| 69-Chapter 11 The Transformer architecture.mp4 |
71.72Мб |
| 7 |
858.51Кб |
| 70 |
991.60Кб |
| 70-Chapter 11 The Transformer encoder.mp4 |
72.54Мб |
| 71 |
657.80Кб |
| 71-Chapter 11 Beyond text classification - Sequence-to-sequence learning.mp4 |
79.61Мб |
| 72 |
40.13Кб |
| 72-Chapter 11 Sequence-to-sequence learning with Transformer.mp4 |
56.14Мб |
| 73 |
435.98Кб |
| 73-Chapter 12 Generative deep learning.mp4 |
80.58Мб |
| 74 |
117.04Кб |
| 74-Chapter 12 How do you generate sequence data.mp4 |
81.89Мб |
| 75 |
445.28Кб |
| 75-Chapter 12 A text-generation callback with variable-temperature sampling.mp4 |
58.46Мб |
| 76 |
24.25Кб |
| 76-Chapter 12 DeepDream.mp4 |
57.37Мб |
| 77 |
512.38Кб |
| 77-Chapter 12 Neural style transfer.mp4 |
80.93Мб |
| 78 |
528.86Кб |
| 78-Chapter 12 Generating images with variational autoencoders.mp4 |
55.81Мб |
| 79 |
208.61Кб |
| 79-Chapter 12 Implementing a VAE with Keras.mp4 |
75.95Мб |
| 8 |
67.45Кб |
| 80 |
688.57Кб |
| 80-Chapter 12 A bag of tricks.mp4 |
62.51Мб |
| 81 |
977.49Кб |
| 81-Chapter 13 Best practices for the real world.mp4 |
62.98Мб |
| 82 |
624.95Кб |
| 82-Chapter 13 Hyperparameter optimization.mp4 |
75.06Мб |
| 83 |
473.27Кб |
| 83-Chapter 13 Scaling-up model training.mp4 |
53.61Мб |
| 84 |
343.28Кб |
| 84-Chapter 13 Multi-GPU training.mp4 |
37.54Мб |
| 85 |
429.12Кб |
| 85-Chapter 13 TPU training.mp4 |
41.58Мб |
| 86 |
866.41Кб |
| 86-Chapter 14 Conclusions.mp4 |
81.16Мб |
| 87 |
355.80Кб |
| 87-Chapter 14 Key enabling technologies.mp4 |
63.57Мб |
| 88 |
508.32Кб |
| 88-Chapter 14 Key network architectures.mp4 |
59.91Мб |
| 89 |
556.40Кб |
| 89-Chapter 14 The limitations of deep learning.mp4 |
60.28Мб |
| 9 |
429.98Кб |
| 90 |
606.43Кб |
| 90-Chapter 14 Local generalization vs. extreme generalization.mp4 |
45.33Мб |
| 91 |
468.29Кб |
| 91-Chapter 14 The purpose of intelligence.mp4 |
53.49Мб |
| 92 |
289.96Кб |
| 92-Chapter 14 Setting the course toward greater generality in AI.mp4 |
69.51Мб |
| 93 |
11.40Кб |
| 93-Chapter 14 Implementing intelligence - The missing ingredients.mp4 |
65.98Мб |
| 94 |
887.20Кб |
| 94-Chapter 14 The missing half of the picture.mp4 |
47.89Мб |
| 95-Chapter 14 Blending together deep learning and program synthesis.mp4 |
58.91Мб |
| 96-Chapter 14 Lifelong learning and modular subroutine reuse.mp4 |
84.14Мб |
| TutsNode.com.txt |
63б |