2.43 GB | 00:12:41 | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
001  Chapter 1 What is deep learning  (28.64 MB)
002  Chapter 1 Learning rules and representations from data  (31.8 MB)
003  Chapter 1 Understanding how deep learning works, in three figures  (38.64 MB)
004  Chapter 1 Before deep learning A brief history of machine learning  (31.1 MB)
005  Chapter 1 Back to neural networks  (29.65 MB)
006  Chapter 1 Why deep learning Why now  (21.63 MB)
007  Chapter 1 Algorithms  (23.69 MB)
008  Chapter 2 The mathematical building blocks of neural networks  (21.33 MB)
009  Chapter 2 Data representations for neural networks  (18.48 MB)
010  Chapter 2 Real-world examples of data tensors  (21.06 MB)
011  Chapter 2 The gears of neural networks Tensor operations  (17.78 MB)
012  Chapter 2 Tensor reshaping  (12.89 MB)
013  Chapter 2 The engine of neural networks Gradient-based optimization  (21.08 MB)
014  Chapter 2 Derivative of a tensor operation The gradient  (29.64 MB)
015  Chapter 2 Chaining derivatives The Backpropagation algorithm  (22.52 MB)
016  Chapter 2 Looking back at our first example  (21.76 MB)
017  Chapter 3 Introduction to Keras and TensorFlow  (31.04 MB)
018  Chapter 3 Setting up a deep learning workspace  (18.56 MB)
019  Chapter 3 First steps with TensorFlow  (29.05 MB)
020  Chapter 3 Anatomy of a neural network Understanding core Keras APIs  (23.67 MB)
021  Chapter 3 The "compile" step Configuring the learning process  (28.77 MB)
022  Chapter 4 Getting started with neural networks Classification and regression  (23.49 MB)
023  Chapter 4 Building your model  (26.74 MB)
024  Chapter 4 Classifying newswires A multiclass classification example  (22.27 MB)
025  Chapter 4 Predicting house prices A regression example  (25.52 MB)
026  Chapter 5 Fundamentals of machine learning  (24.74 MB)
027  Chapter 5 The nature of generalization in deep learning  (35.72 MB)
028  Chapter 5 Evaluating machine learning models  (31.74 MB)
029  Chapter 5 Improving model fit  (17.17 MB)
030  Chapter 5 Improving generalization  (30.17 MB)
031  Chapter 5 Regularizing your model  (27.02 MB)
032  Chapter 6 The universal workflow of machine learning  (29.56 MB)
033  Chapter 6 Collect a dataset  (39.7 MB)
034  Chapter 6 Develop a model  (19.56 MB)
035  Chapter 6 Beat a baseline  (17.67 MB)
036  Chapter 6 Deploy the model  (34.66 MB)
037  Chapter 6 Monitor your model in the wild  (15.34 MB)
038  Chapter 7 Working with Keras A deep dive  (28.8 MB)
039  Chapter 7 Subclassing the Model class  (14.92 MB)
040  Chapter 7 Using built-in training and evaluation loops  (24.87 MB)
041  Chapter 7 Writing your own training and evaluation loops  (19.55 MB)
042  Chapter 7 Make it fast with tf function  (15.48 MB)
043  Chapter 8 Introduction to deep learning for computer vision  (17.86 MB)
044  Chapter 8 The convolution operation  (30.83 MB)
045  Chapter 8 Training a convnet from scratch on a small dataset  (28.96 MB)
046  Chapter 8 Data preprocessing  (25.63 MB)
047  Chapter 8 Leveraging a pretrained model  (28.95 MB)
048  Chapter 8 Feature extraction with a pretrained model  (27.37 MB)
049  Chapter 9 Advanced deep learning for computer vision  (42.23 MB)
050  Chapter 9 Modern convnet architecture patterns  (26.84 MB)
051  Chapter 9 Residual connections  (24.78 MB)
052  Chapter 9 Depthwise separable convolutions  (30.46 MB)
053  Chapter 9 Interpreting what convnets learn  (22.91 MB)
054  Chapter 9 Visualizing convnet filters  (17.15 MB)
055  Chapter 9 Visualizing heatmaps of class activation  (19.59 MB)
056  Chapter 10 Deep learning for timeseries  (23.77 MB)
057  Chapter 10 Preparing the data  (19.53 MB)
058  Chapter 10 Let's try a basic machine learning model  (19.93 MB)
059  Chapter 10 Understanding recurrent neural networks  (17.22 MB)
060  Chapter 10 A recurrent layer in Keras  (17.59 MB)
061  Chapter 10 Advanced use of recurrent neural networks  (25.96 MB)
062  Chapter 10 Using bidirectional RNNs  (28.63 MB)
063  Chapter 11 Deep learning for text  (25.89 MB)
064  Chapter 11 Preparing text data  (19.97 MB)
065  Chapter 11 Vocabulary indexing  (21.4 MB)
066  Chapter 11 Two approaches for representing groups of words Sets and sequences  (34.45 MB)
067  Chapter 11 Processing words as a sequence The sequence model approach, Part 1  (31.01 MB)
068  Chapter 11 Processing words as a sequence The sequence model approach, Part 2  (23.32 MB)
069  Chapter 11 The Transformer architecture  (30.88 MB)
070  Chapter 11 The Transformer encoder  (30.75 MB)
071  Chapter 11 Beyond text classification Sequence-to-sequence learning  (35.34 MB)
072  Chapter 11 Sequence-to-sequence learning with Transformer  (25.81 MB)
073  Chapter 12 Generative deep learning  (35.98 MB)
074  Chapter 12 How do you generate sequence data  (36.07 MB)
075  Chapter 12 A text-generation callback with variable-temperature sampling  (26.5 MB)
076  Chapter 12 DeepDream  (25.84 MB)
077  Chapter 12 Neural style transfer  (35.66 MB)
078  Chapter 12 Generating images with variational autoencoders  (22.42 MB)
079  Chapter 12 Implementing a VAE with Keras  (32.76 MB)
080  Chapter 12 A bag of tricks  (26 MB)
081  Chapter 13 Best practices for the real world  (27.14 MB)
082  Chapter 13 Hyperparameter optimization  (33.82 MB)
083  Chapter 13 Scaling-up model training  (22.97 MB)
084  Chapter 13 Multi-GPU training  (15.65 MB)
085  Chapter 13 TPU training  (18.16 MB)
086  Chapter 14 Conclusions  (36.42 MB)
087  Chapter 14 Key enabling technologies  (28.31 MB)
088  Chapter 14 Key network architectures  (26.06 MB)
089  Chapter 14 The limitations of deep learning  (27.45 MB)
090  Chapter 14 Local generalization vs  extreme generalization  (19.3 MB)
091  Chapter 14 The purpose of intelligence  (24.22 MB)
092  Chapter 14 Setting the course toward greater generality in AI  (31.16 MB)
093  Chapter 14 Implementing intelligence The missing ingredients  (28.93 MB)
094  Chapter 14 The missing half of the picture  (21.99 MB)
095  Chapter 14 Blending together deep learning and program synthesis  (26.9 MB)
096  Chapter 14 Lifelong learning and modular subroutine reuse  (38.13 MB)
[align=center]
Screenshot

[/align]

https://rapidgator.net/file/ff6a5199b220b6875fb279c493fe3761/Oreilly_Deep_Learning_with_Python_Second_Edition_Video_Edition.part1.rar
https://rapidgator.net/file/f39904ffc84a9edec22e12bd05e1f5f2/Oreilly_Deep_Learning_with_Python_Second_Edition_Video_Edition.part2.rar

https://filestore.me/f2aanqjsi5lh/Oreilly_Deep_Learning_with_Python_Second_Edition_Video_Edition.part1.rar
https://filestore.me/pbabkyzl908k/Oreilly_Deep_Learning_with_Python_Second_Edition_Video_Edition.part2.rar