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

$43.99

4.4 (11 Ratings)

Paperback Feb 2020 512 pages 2nd Edition

Rowel Atienza

$43.99

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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Key benefits

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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Amazon verified reviews

Ryan Zurrin Apr 21, 2022

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

Amazon Verified review

DM Jun 04, 2021

This book gives a very quick introduction to the core ML models (MLP, CNN, RNN), and I was looking forward to learning about advanced techniques for each of them. I'm an NLP engineer, and so was rather disappointed to see that the rest of the book is aimed almost entirely at computer vision techniques, with some reinforcement learning towards the end. Sadly this book provided me with nothing new in terms of natural language processing techniques.Also the explanations aren't very detailed, so you will probably need to purchase a companion book to go alongside this, as it's mostly discussed from a practical application with lots of assumed knowledge (even about the more advanced topics).Finally, the written form/language coherence is slightly off, which isn't helpful when trying to get your head around the very brief explanations of advanced concepts.

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.

Dr. Bernd M. Feb 25, 2021

Für mich bisher das beste Buch für diejenigen, die ernsthaft mit Deep-Learning-Konzepten arbeiten möchten und sich schon etwas mit den Grundkonzepten des Deep Learning auskennen. Die meisten anderen Bücher kratzen da mit allgemeinen Einführungen eher an der Oberfläche oder behandeln nur ganz spezielle Fragestellungen, die schon jahrelange vorherige Praxis voraussetzen. In diesem Buch wird klar derjenige Leser angesprochen, der die Grundlagen beherrscht und ein bisschen mehr in die Tiefe gehen will bzw. eigene Anwendungen erstellen will. Ich finde, hierzu wurde eine gute Mischung aus theoretischen Grundlagen und Anwendungsbeispielen gefunden, um sich schnell in die Thematik einzuarbeiten. Für Neueinsteiger in dem Thema ist es aber m. M. n. zu anspruchsvoll, da die Grundlagen vorausgesetzt werden.

Harold M. Martin: No problem has been fixed. Sep 23, 2020

I am running Python 3.8 on Mac Catalina using PyCharm. I successfully installed Tensorflow previously and it seemed to be working well. When I tried the first few lines of the code given for downloading the MNIST database multiple exceptions were thrown indicating that for some reason an SSL certificate could not be obtained. Safari had no problem downloading it given the URL in the error message. Perhaps Tensorflow can't do well with https. So, I was unable to do anything with the MNIST database. Since this is the subject of the book I can't use the book.

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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