Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices
Key Features
- Introduces and then uses TensorFlow 2 and Keras right from the start
- Teaches key machine and deep learning techniques
- Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples
Book Description
Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and 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 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 (CNNs), 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 and the Keras API
- Use Regression analysis, the most popular approach to machine learning
- Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers
- Use GANs (generative adversarial networks) to create new data that fits with existing patterns
- Discover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another
- Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response
- Train your models on the cloud and put TF to work in real environments
- Explore how Google 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, and AutoML to build machine learning systems.
Table of Contents
- Neural Network Foundations with TensorFlow 2.0
- TensorFlow 1.x and 2.x
- Regression
- Convolutional Neural Networks
- Advanced Convolutional Neural Networks
- Generative Adversarial Networks
- Word Embeddings
- Recurrent Neural Networks
- Autoencoders
- Unsupervised Learning
- Reinforcement Learning
- TensorFlow and Cloud
- TensorFlow for Mobile and IoT and TensorFlow.js
- An introduction to AutoML
- The Math Behind Deep Learning
- Tensor Processing Unit
Antonio Gulli has a passion for establishing and managing global technological talent, for innovation and execution. His core expertise is in cloud computing, deep learning, and search engines. Currently, he serves as the Engineering Director for the Office of the CTO, Google Cloud. Previously, he served as Google Warsaw Site leader doubling the size of the engineering site.
Amita Kapoor is an Associate Professor at the Department of Electronics, SRCASW, University of Delhi. She has been teaching neural networks for twenty years. During her PhD, she was awarded the prestigious DAAD fellowship, which enabled her to pursue part of her research work at the Karlsruhe Institute of Technology, Germany. She was awarded the Best Presentation Award at the International Conference on Photonics 2008. Being a member of the ACM, IEEE, INNS, and ISBS, she has published more than 40 papers in international journals and conferences. Her research areas include machine learning, AI, neural networks, robotics, and Buddhism and ethics in AI. She has co-authored the book, Tensorflow 1.x Deep Learning Cookbook, by Packt Publishing.
Sujit Pal is a technology research director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group. His areas of interest include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora. In addition to co-authoring a book on deep learning with Antonio Gulli, Sujit writes about technology on his blog, Salmon Run.
