This is a completely updated edition of a guide to the advanced deep learning techniques, revised for TensorFlow 2.x. In this edition author Rowel Atienza introduces the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet). Using Keras as an open-source deep learning library, the book features hands-on projects. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders.

<ASIN:1838821651>

Author: Rowel Atienza
Publisher: Packt Publishing
Date: February 2020
Pages: 512
ISBN: 978-1838821654
Print: 1838821651
Kindle: B0851D5YQQ
Audience: TensorFlow developers
Level: Intermediate/Advanced
Category: Artificial Intelligence 

• 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

In his review of the 1st edition, Mike James awarded a rating of 4.5 out of 5 to this book which presents advanced examples and is strong on GANs.

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