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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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Frequently bought together

Advanced Deep Learning with TensorFlow 2 and Keras

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Deep Learning with TensorFlow 2 and Keras

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Total $ 131.97

Rhandley Cajote Mar 16, 2020

This book provides a good introduction of advanced deep learning concepts such as GAN's, autoebcoders and reinforcement learning and other important concepts in deep learning. The discussions are very general with concise details and sample codes to demonstrate the concepts. Highly recommended to those that are just beginning to study these areas.

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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.

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Akshit shah Apr 09, 2020

This book is fantastic for all individual who want to dig deep into the Deep Learning concepts, the book is highly simplified and practical. Its a must pick for all Deep learning folks out there

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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.

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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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