Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more , Second Edition

Rowel Atienza

€32.99

4.4 (11 Ratings)

Paperback Feb 2020 512 pages 2nd Edition

Rowel Atienza

€32.99

4.4 (11 Ratings)

Paperback Feb 2020 512 pages 2nd Edition

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Advanced Deep Learning with TensorFlow 2 and Keras

Deep Neural Networks

In this chapter, we'll be examining deep neural networks. These networks have shown excellent performance in terms of the accuracy of their classification on more challenging datasets like ImageNet, CIFAR10 (https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf), and CIFAR100. For conciseness, we'll only be focusing on two networks: ResNet [2][4] and DenseNet [5]. While we will go into much more detail, it's important to take a minute to introduce these networks.

ResNet introduced the concept of residual learning, which enabled it to build very deep networks by addressing the vanishing gradient problem (discussed in section 2) in deep convolutional networks.

DenseNet improved ResNet further by allowing every convolution to have direct access to inputs, and lower layer feature maps. It's also managed to keep the number of parameters low in deep networks by utilizing both the Bottleneck and Transition layers...

3. ResNet v2

The improvements for ResNet v2 are mainly found in the arrangement of layers in the residual block as shown in Figure 2.3.1.

The prominent changes in ResNet v2 are:

The use of a stack of 1 x 1 – 3 x 3 – 1 × 1 BN-ReLU-Conv2D
Batch normalization and ReLU activation come before two dimensional convolution

Figure 2.3.1: A comparison of residual blocks between ResNet v1 and ResNet v2

ResNet v2 is also implemented in the same code as resnet-cifar10-2.2.1.py, as can be seen in Listing 2.2.1:

Listing 2.2.1: resnet-cifar10-2.2.1.py

def resnet_v2(input_shape, depth, num_classes=10):
    """ResNet Version 2 Model builder [b]

    Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or 
    also known as bottleneck layer.
    First shortcut connection per layer is 1 x 1 Conv2D.
    Second and onwards shortcut connection is identity.
    At the beginning of each stage, 
    the feature map size...

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Explore the most advanced deep learning techniques that drive modern AI results
New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation
Completely updated for TensorFlow 2.x

Description

Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.

Who is this book for?

This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.

What you will learn

Use mutual information maximization techniques to perform unsupervised learning
Use segmentation to identify the pixel-wise class of each object in an image
Identify both the bounding box and class of objects in an image using object detection
Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs
Understand deep neural networks - including ResNet and DenseNet
Understand and build autoregressive models – autoencoders, VAEs, and GANs
Discover and implement deep reinforcement learning methods

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WU. Mar 20, 2020

Unlike other authors that release new editions yearly with few revisions, Atienza has packed more than 40% worth of content versus the 1st edition. This is a first for me.The 1st edition is just about identical to the 2nd up to chapter 10, save for some stylistic changes, but half of chapter 10, as well as chapters 11, 12, 13, are brand new.Everything that made the 1st edition great is till here: great exposition of the fundamentals, the math for those that'd like to dig a bit deeper, the great references, as well as clean, understandable, working code.This book is highly recommended as a reference for those looking to move into deeper waters in the production of ML models for use cases that would strain conventional Neural Networks.If I could, however, make a suggestion to all authors of ML books, it would be to PLEASE move away from MNIST, and the CIFAR datasets. I know these are the standard benchmarks in the field but you could just as easily cite this in your repositories and use the real estate in your books to explore other use cases such as anomaly detection in categorical data, time series, etc. This would make your books much more valuable in the hands of industry practitioners not looking to work strictly with images. So let's stop beating those horses yes?Highly Recommended!

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Matthew Emerick Apr 14, 2020

Disclaimer: The publisher and asked me to review this book and gave me a review copy. I promise to be 100% honest in how I feel about this book, both the good and the less so.Overview:This book is for the advanced student or experienced practitioner of artificial intelligence. It assumes you have a general level of knowledge of implementing various neural network architectures, or at least a good understanding. One caveat I would very much like to point out is that the Preface states that most of the code requires use of a GPU, which is not mentioned in the book's description. Thankfully, there are free options which mitigate this requirement, which can be found through a simple web search.What I Like:The introductory chapter gives enough information on Keras to allow even a novice AI developer to work through the book and get a lot out of it. As someone in the middle of beginner and expert, I appreciate things like that, as they can the reader a refresher of the material in case they haven't used it in a while. In this case, the author thoughtfully runs through examples using common neural network architectures, such as the multilayer perceptron, recurrent neural network, and convolutional neural network. Each section of the review also gives a refresher of the math at just the right level to get the reader ready for the advanced material later in the book. A final reason to appreciate the first chapter is that is gives clear and concise definitions of common terms used by AI practitioners.The overall organization is also well done. I appreciate a book that flows well, as it greatly adds to the understanding when learning from it as well as lends to finding information easier when using it as a reference. Each chapter adds to the complexity of the reader's understanding, but gradually enough to aid with comprehension.What I Don't LikeThis is a difficult section to write. It's not that there is too much to write about, bu the opposite. This book is well written, both by structure and by prose. It's a balanced book to learn from and to refer to. It's a practitioner's book in that it focuses on code over math, but covers both. The topics covered are useful, interesting, and wide ranging. I did mention at the beginning the GPU requirement, but that's all I'm seeing right now.What I Would Like to SeeThis is a great book overall. I would like to see more, either by expanding the book or writing another one. At 512 pages and the number of topics that could have been covered in addition to what is already on there, I would have been fine with the book being longer.Overall, I give this book a 4.9 out of 5 stars. The author did an excellent job writing this book. I can find little fault.

Prayson Daniel Apr 21, 2020

Advanced Deep Learning with TensorFlow 2 and Keras is a high-level introduction to Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). This book is a powerful tool for AI practitioners that already have knowledge of Deep Learning but wish to understand MLP, CNN, and RNN in a technical sense, namely building and training such models.The first chapters introduce basic of Python library Keras via TensorFlow API as a tool for building models architectures. Concepts of such as regularisation, regularisation, performance evaluation, model structures are presented in a way that both novice and a Deep Learning practitioners would find valuable.There is a clear flow of ideas as the author moves from basics to advance models building. This book could have been longer. Natural Language Processing and Audio Processing are topics that are not covered. Nevertheless, what is covered is covered very well.

Alessandro Apr 27, 2021

That's really what I needed. A very well written book an advanced deep learning techniques. I have mostly focused my attention on autoencoders and Gans, but the other chapters sound interesting either.

Ryan Zurrin Apr 21, 2022

Great reference for anyone looking to get into deep learning and working with tensorflow

Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more , Second Edition

What do you get with Print?

Advanced Deep Learning with TensorFlow 2 and Keras

Deep Neural Networks

1. Functional API

2. Deep Residual Network (ResNet)

3. ResNet v2

4. Densely Connected Convolutional Network (DenseNet)

5. Conclusion

6. References

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Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more , Second Edition

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