Deep Learning with Python Second Edition Video Edition | 1C16E25DB1A57A72F92559E43D4299BB67029D76

Deep Learning with Python Second Edition Video Edition

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[TutsNode.com] - Deep Learning with Python, Second Edition, Video Edition/
01-Chapter 1 What is deep learning.mp4	80.5 MB
02-Chapter 1 Learning rules and representations from data.mp4	87.4 MB
03-Chapter 1 Understanding how deep learning works, in three figures.mp4	107.9 MB
04-Chapter 1 Before deep learning - A brief history of machine learning.mp4	86.7 MB
05-Chapter 1 Back to neural networks.mp4	81.7 MB
06-Chapter 1 Why deep learning Why now.mp4	59.2 MB
07-Chapter 1 Algorithms.mp4	65.4 MB
08-Chapter 2 The mathematical building blocks of neural networks.mp4	54.6 MB
09-Chapter 2 Data representations for neural networks.mp4	49.9 MB
10-Chapter 2 Real-world examples of data tensors.mp4	50.9 MB
11-Chapter 2 The gears of neural networks - Tensor operations.mp4	48.8 MB
12-Chapter 2 Tensor reshaping.mp4	38.5 MB
13-Chapter 2 The engine of neural networks - Gradient-based optimization.mp4	52.4 MB
14-Chapter 2 Derivative of a tensor operation - The gradient.mp4	72.8 MB
15-Chapter 2 Chaining derivatives - The Backpropagation algorithm.mp4	57.7 MB
16-Chapter 2 Looking back at our first example.mp4	56.3 MB
17-Chapter 3 Introduction to Keras and TensorFlow.mp4	70.9 MB
18-Chapter 3 Setting up a deep learning workspace.mp4	45.7 MB
19-Chapter 3 First steps with TensorFlow.mp4	72.5 MB
20-Chapter 3 Anatomy of a neural network - Understanding core Keras APIs.mp4	58.8 MB
21-Chapter 3 The “compile” step - Configuring the learning process.mp4	71.3 MB
22-Chapter 4 Getting started with neural networks - Classification and regression.mp4	56.0 MB
23-Chapter 4 Building your model.mp4	67.3 MB
24-Chapter 4 Classifying newswires - A multiclass classification example.mp4	59.6 MB
25-Chapter 4 Predicting house prices - A regression example.mp4	64.8 MB
26-Chapter 5 Fundamentals of machine learning.mp4	59.7 MB
27-Chapter 5 The nature of generalization in deep learning.mp4	84.4 MB
28-Chapter 5 Evaluating machine learning models.mp4	76.1 MB
29-Chapter 5 Improving model fit.mp4	42.5 MB
30-Chapter 5 Improving generalization.mp4	72.7 MB
31-Chapter 5 Regularizing your model.mp4	63.7 MB
32-Chapter 6 The universal workflow of machine learning.mp4	70.2 MB
33-Chapter 6 Collect a dataset.mp4	89.2 MB
34-Chapter 6 Develop a model.mp4	46.5 MB
35-Chapter 6 Beat a baseline.mp4	43.2 MB
36-Chapter 6 Deploy the model.mp4	82.0 MB
37-Chapter 6 Monitor your model in the wild.mp4	36.8 MB
38-Chapter 7 Working with Keras - A deep dive.mp4	73.3 MB
39-Chapter 7 Subclassing the Model class.mp4	36.8 MB
40-Chapter 7 Using built-in training and evaluation loops.mp4	63.1 MB
41-Chapter 7 Writing your own training and evaluation loops.mp4	48.0 MB
42-Chapter 7 Make it fast with tf.function.mp4	37.7 MB
43-Chapter 8 Introduction to deep learning for computer vision.mp4	42.6 MB
44-Chapter 8 The convolution operation.mp4	78.0 MB
45-Chapter 8 Training a convnet from scratch on a small dataset.mp4	70.3 MB
46-Chapter 8 Data preprocessing.mp4	64.7 MB
47-Chapter 8 Leveraging a pretrained model.mp4	68.3 MB
48-Chapter 8 Feature extraction with a pretrained model.mp4	67.5 MB
49-Chapter 9 Advanced deep learning for computer vision.mp4	104.6 MB
50-Chapter 9 Modern convnet architecture patterns.mp4	61.6 MB
51-Chapter 9 Residual connections.mp4	60.1 MB
52-Chapter 9 Depthwise separable convolutions.mp4	71.2 MB
53-Chapter 9 Interpreting what convnets learn.mp4	61.4 MB
54-Chapter 9 Visualizing convnet filters.mp4	42.4 MB
55-Chapter 9 Visualizing heatmaps of class activation.mp4	77.9 MB
56-Chapter 10 Deep learning for timeseries.mp4	56.5 MB
57-Chapter 10 Preparing the data.mp4	49.3 MB
58-Chapter 10 Let’s try a basic machine learning model.mp4	47.2 MB
59-Chapter 10 Understanding recurrent neural networks.mp4	42.4 MB
60-Chapter 10 A recurrent layer in Keras.mp4	43.7 MB
61-Chapter 10 Advanced use of recurrent neural networks.mp4	62.8 MB
62-Chapter 10 Using bidirectional RNNs.mp4	68.0 MB
63-Chapter 11 Deep learning for text.mp4	60.7 MB
64-Chapter 11 Preparing text data.mp4	48.7 MB
65-Chapter 11 Vocabulary indexing.mp4	52.8 MB
66-Chapter 11 Two approaches for representing groups of words - Sets and sequences.mp4	83.7 MB
67-Chapter 11 Processing words as a sequence - The sequence model approach, Part 1.mp4	74.0 MB
68-Chapter 11 Processing words as a sequence - The sequence model approach, Part 2.mp4	55.1 MB
69-Chapter 11 The Transformer architecture.mp4	75.2 MB
70-Chapter 11 The Transformer encoder.mp4	76.1 MB
71-Chapter 11 Beyond text classification - Sequence-to-sequence learning.mp4	83.5 MB
72-Chapter 11 Sequence-to-sequence learning with Transformer.mp4	58.9 MB
73-Chapter 12 Generative deep learning.mp4	84.5 MB
74-Chapter 12 How do you generate sequence data.mp4	85.9 MB
75-Chapter 12 A text-generation callback with variable-temperature sampling.mp4	61.3 MB
76-Chapter 12 DeepDream.mp4	60.2 MB
77-Chapter 12 Neural style transfer.mp4	84.9 MB
78-Chapter 12 Generating images with variational autoencoders.mp4	58.5 MB
79-Chapter 12 Implementing a VAE with Keras.mp4	79.6 MB
80-Chapter 12 A bag of tricks.mp4	65.5 MB
81-Chapter 13 Best practices for the real world.mp4	66.0 MB
82-Chapter 13 Hyperparameter optimization.mp4	78.7 MB
83-Chapter 13 Scaling-up model training.mp4	56.2 MB
84-Chapter 13 Multi-GPU training.mp4	39.4 MB
85-Chapter 13 TPU training.mp4	43.6 MB
86-Chapter 14 Conclusions.mp4	85.1 MB
87-Chapter 14 Key enabling technologies.mp4	66.7 MB
88-Chapter 14 Key network architectures.mp4	62.8 MB
89-Chapter 14 The limitations of deep learning.mp4	63.2 MB
90-Chapter 14 Local generalization vs. extreme generalization.mp4	47.5 MB
91-Chapter 14 The purpose of intelligence.mp4	56.1 MB
92-Chapter 14 Setting the course toward greater generality in AI.mp4	72.9 MB
93-Chapter 14 Implementing intelligence - The missing ingredients.mp4	69.2 MB
94-Chapter 14 The missing half of the picture.mp4	50.2 MB
95-Chapter 14 Blending together deep learning and program synthesis.mp4	61.8 MB
96-Chapter 14 Lifelong learning and modular subroutine reuse.mp4	88.2 MB
Total files 98