Deep Learning with TensorFlow 2.0 and Keras: Regression, ConvNets, GANs, RNNs, NLP & more with TF 2.0 and the Keras API, 2nd Edition
Build machine and deep learning systems with the newly released TensorFlow 2.0 and Keras for the lab, production, and mobile devices
Key Features
Introduces and then uses TensorFlow 2.0 and Keras right from the start
Teaches key machine and deep learning techniques
Covers theory and practice with clear explanations and extensive code samples
Book Description
Deep Learning with TensorFlow 2.0 and Keras – Second Edition teaches deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.
TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2.0 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.
This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets, GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
What you will learn
Build machine learning and deep learning systems with TensorFlow 2.0 and the Keras API
Regression – the most widely used approach to machine learning
ConvNets – convolutional neural networks, essential for deep learning systems such as image classifiers
GANs – generative adversarial networks that use deep learning techniques to create new data that fits with existing patterns
RNNs – recurrent neural networks that can process sequences of input intelligently, using one part of a sequence to correctly interpret another
NLP – apply deep learning to natural human language – interpreting natural language texts to produce an appropriate response
TF and Cloud in production — learn to train your models on the cloud put TF to work in real environments
Maths behind deep learning — learn the mathematics behind your models’ training
TF on mobile — use the lightweight mobile TF libraries to build on-device machine intelligence into Android and iOS apps
AutoML – Exploring how Google’s tools can automate simple ML workflows without the need for complex modeling
Who This Book Is For
This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. Whether or not you have done machine learning before, this book gives you the theory and practice required to use Keras, TensorFlow 2.0, and AutoML to build machine learning systems.
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