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Ajay Baranwal - What’s New in TensorFlow 2.0: Use the new and improved features of TensorFlow to enhance machine learning and deep learning

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Get to grips with key structural changes in TensorFlow 2.0TensorFlow is an end-to-end machine learning platform for experts as well as beginners, and its new version, TensorFlow 2.0 (TF 2.0), improves its simplicity and ease of use. This book will help you understand and utilize the latest TensorFlow features.Whats New in TensorFlow 2.0 starts by focusing on advanced concepts such as the new TensorFlow Keras APIs, eager execution, and efficient distribution strategies that help you to run your machine learning models on multiple GPUs and TPUs. The book then takes you through the process of building data ingestion and training pipelines, and it provides recommendations and best practices for feeding data to models created using the new tf.keras API. Youll explore the process of building an inference pipeline using TF Serving and other multi-platform deployments before moving on to explore the newly released AIY, which is essentially do-it-yourself AI. This book delves into the core APIs to help you build unified convolutional and recurrent layers and use TensorBoard to visualize deep learning models using what-if analysis.By the end of the book, youll have learned about compatibility between TF 2.0 and TF 1.x and be able to migrate to TF 2.0 smoothly.What you will learn Implement tf.keras APIs in TF 2.0 to build, train, and deploy production-grade models Build models with Keras integration and eager execution Explore distribution strategies to run models on GPUs and TPUs Perform what-if analysis with TensorBoard across a variety of models Discover Vision Kit, Voice Kit, and the Edge TPU for model deployments Build complex input data pipelines for ingesting large training datasets

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What's New in TensorFlow 2.0
Use the new and improved features of TensorFlow to enhance machine learning and deep learning
Ajay Baranwal
Alizishaan Khatri
Tanish Baranwal

BIRMINGHAM - MUMBAI Whats New in TensorFlow 20 Copyright 2019 Packt - photo 2

BIRMINGHAM - MUMBAI
What's New in TensorFlow 2.0

Copyright 2019 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

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Contributors
About the authors

Ajay Baranwal works as a director at the Center for Deep Learning in Electronics Manufacturing, where he is responsible for researching and developing TensorFlow-based deep learning applications in the semiconductor and electronics manufacturing industry. Part of his role is to teach and train deep learning techniques to professionals.

He has a solid history of software engineering and management, where he got hooked on deep learning. He moved to natural language understanding (NLU) to pursue deep learning further at Abzooba and built an information retrieval system for the finance sector. He has also worked at Ansys Inc. as a senior manager (engineering) and a technical fellow (data science) and introduced several ML applications....

About the reviewers

Jay Kim is an experienced data scientist who has broad experience in data science, AI, machine learning, deep learning, and statistical analysis. He has broad experience in various industries, such as utilities, the automotive sector, manufacturing, commercial, and research.

Narotam Singh has been actively involved in various technical programs and the training of Government of India (GoI) officers in the fields of information technology and communication. He did his master's degree in the field of electronics, and graduated with honors in physics. He also holds a diploma in computer engineering and a postgraduate diploma in computer applications. Presently, he works as a freelancer. He has many research publications to his name and is also a technical reviewer of various books. His present research interests involve artificial intelligence, machine learning, deep learning, robotics, and spirituality.

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What this book covers

, Getting Started with TensorFlow 2.0 , provides a quick bird's-eye view of the architectural and API-level changes in TensorFlow 2.0. It covers TensorFlow 2.0 installation and setup, compares how it has changed compared to TensorFlow 1.x (such as Keras APIs and layer APIs), and also presents the addition of rich extensions such as TensorFlow Probability, Tensor2Tensor, Ragged Tensors, and the newly available custom training logic for loss functions.

, Keras Default Integration and Eager Execution , goes deeper into high-level TensorFlow 2.0 APIs using Keras. It presents a detailed perspective of how graphs are evaluated in TensorFlow 1.x compared to TensorFlow 2.0. It explains lazy evaluation and eager execution and how they are different in TensorFlow 2.0, and it also shows how to use Keras model subclassing to incorporate TensorFlow 2.0 lower APIs for custom-built models.

, Designing and Constructing Input Data Pipelines , gives an overview of how to build complex input data pipelines for ingesting large training and inference datasets in most common formats, such as CSV, images, and text using TFRecords and tf.data.Dataset. It gives a general explanation of protocol buffers and protocol messages and how are they implemented using tf.Example. It also explains the best practices of using tf.data.Dataset with regard to the shuffling, prefetching, and batching of data, and provides recommendations for building data pipelines.

, Model Training and Use of TensorBoard , covers an overall model training pipeline to enable you to build, train, and validate state-of-the-art models. It talks about how to integrate input data pipelines, create tf.keras models, run training in a distributed manner, and run validations to fine-tune hyperparameters. It explains how to export TensorFlow models for deployment or inferencing, and it outlines the usage of TensorBoard, the changes to it in TensorFlow 2.0, and how to use it for debugging and profiling a model's speed and performance.

, Model Inference Pipelines Multi-platform Deployments , shows us some deployment strategies for using the trained model to build software applications at scale in a live production environment. Models trained in TensorFlow 2.0 can be deployed on platforms such as servers and web browsers using a variety of programming languages, such as Python and JavaScript.

, AIY Projects and TensorFlow Lite , shows us how to deploy models trained in TensorFlow 2.0 on low-powered embedded systems such as edge devices and mobile systems including Android, iOS, the Raspberry Pi, Edge TPUs, and the NVIDIA Jetson Nano. It also contains details about training and deploying models on Google's AIY kits.

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