Book : Advanced Deep Learning With Tensorflow 2 And Keras..

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Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and KerasKey FeaturesExplore 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.xBook DescriptionAdvanced 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, youll 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. Youll 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 learnUse 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 methodsWho this book is forThis 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.Table of ContentsIntroducing Advanced Deep Learning with KerasDeep Neural NetworksAutoencodersGenerative Adversarial Networks (GANs)Improved GANsDisentangled Representation GANsCross-Domain GANsVariational Autoencoders (VAEs)Deep Reinforcement LearningPolicy Gradient MethodsObject DetectionSemantic SegmentationUnsupervised Learning Using Mutual Information Review Great visuals, code, and math. The book delivers what the deep learning practitioner needs: advanced content with replicable and reproducible results. I highly recommend this great book by Rowel Atienza. --Bernardo F. Nunes, PhD, Lead Data Scientist, Growth Tribe AcademyAdvanced Deep Learning with TensorFlow 2 and Keras - Second Edition is a good and big step into an advanced practice direction. Its a brilliant book and consider this as a must-read for all. --Dr. Tristan Behrens, Founding Member of AI Guild and Independent Deep Learning Hands-On AdviserI highly recommend this book for the curious data practitioner who wants to further solidify their knowledge of deep learning. The companion GitHub code repository is very useful and provides a hassle-free way to actually experiment with the various ideas presented in the book. If you enjoy reading technical books, but also enjoy experimenting with real code, and didnt think the two could be combined effectively - this boo
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