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You're reading from What's New in TensorFlow 2.0 Use the new and improved features of TensorFlow to enhance machine learning and deep learning

Product type Paperback

Published in Aug 2019

Publisher Packt

ISBN-13 9781838823856

Length 202 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Deep Learning

Authors (3):

Tanish Baranwal

Alizishaan Khatri

Ajay Baranwal

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Table of Contents (13) Chapters

Preface

1. Section 1: TensorFlow 2.0 - Architecture and API Changes

2. Getting Started with TensorFlow 2.0 FREE CHAPTER

3. Keras Default Integration and Eager Execution

4. Section 2: TensorFlow 2.0 - Data and Model Training Pipelines

5. Designing and Constructing Input Data Pipelines

6. Model Training and Use of TensorBoard

7. Section 3: TensorFlow 2.0 - Model Inference and Deployment and AIY

8. Model Inference Pipelines - Multi-platform Deployments

9. AIY Projects and TensorFlow Lite

10. Section 4: TensorFlow 2.0 - Migration, Summary

11. Migrating From TensorFlow 1.x to 2.0

12. Other Books You May Enjoy

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Summary

This chapter has shown an overall approach to designing and constructing an input data pipeline using TF 2.0 APIs in a simple and suggestive manner. It has provided the building blocks of the different components of the data pipeline and given details of the APIs that are required to build the pipeline. A comparison between TF 1.x APIs and TF 2.0 APIs has been provided.

The overall flow can be summarized in two major passes: raw data management and dataset manipulation. Raw data management deals with raw data; splitting data into train, validation, and test sets; and the creation of TFRecords. Typically, this is a one-time process, which can also include offline data transformation. Dataset manipulation is an online transformation process that creates dataset objects, applies transformations, shuffles the data, and then repeats this and creates batches of the data with...

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Authors (2)

Baranwal

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.

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Alizishaan Khatri

Alizishaan Khatri works as a machine learning engineer in Silicon Valley. He uses TensorFlow to build, design, and maintain production-grade systems that use deep learning for NLP applications. A major system he has built is a deep learning-based system for detecting offensive content in chats. Other works he has done includes text classification and named entity recognition (NER) systems for different use cases. He is passionate about sharing ideas with the community and frequently speaks at tech conferences across the globe. He holds a master's degree in computer science from the SUNY Buffalo University. His thesis proposed a solution to the problem of overfitting in deep learning. Outside of his work, he enjoys skiing and mountaineering.

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