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Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python de Pramod Singh

Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python

Autor Pramod Singh, Avinash Manure
en Limba Engleză Paperback – 5 ian 2020
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. 

The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. 

You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways.

What You'll Learn
  • Review the new features of TensorFlow 2.0
  • Use TensorFlow 2.0 to build machine learning and deep learning models 
  • Perform sequence predictions using TensorFlow 2.0
  • Deploy TensorFlow 2.0 models with practical examples

Who This Book Is For

Data scientists, machine and deep learning engineers.


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Specificații

ISBN-13: 9781484255605
ISBN-10: 1484255607
Pagini: 164
Ilustrații: XVI, 164 p. 126 illus.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.26 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States

Cuprins

Chapter 1:  Introduction to TensorFlow 2.0.- Chapter 2: Supervised Learning with TensorFlow 2.0.- Chapter 3: Neural Networks and Deep Learning with TensorFlow 2.0.- Chapter 4: Images with TensorFlow 2.0.- Chapter 5: NLP Modeling with TensorFlow 2.0.- Chapter 6: TensorFlow Models in Production. 


Notă biografică

Pramod Singh is currently playing a role of Machine Learning Expert at Walmart Labs. He has extensive hands-on experience in machine learning, deep learning, AI, data engineering, designing algorithms and application development. He has spent more than 10 years working on multiple data projects at different organizations. He’s the author of three books -Machine Learning with PySpark , Learn PySpark and Learn TensorFlow 2.0. He is also a regular speaker at major conferences such as O’Reilly’s Strata and AI conferences. Pramod holds a BTech in electrical engineering from  B.A.T.U, and an  MBA from Symbiosis University. He has also done Data Science certification from IIM–Calcutta. He lives in Bangalore with his wife and three-year-old son. In his spare time, he enjoys playing guitar, coding, reading, and watching football.

Avinash Manure is a Senior Data Scientist at Publicis Sapient with over 8 years of experience in solving real-world business challenges using Data. He is proficient in deploying complex machine learning and statistical modeling algorithms/techniques for identifying patterns and extracting valuable insights for key stakeholders and organizational leadership.Avinash holds a bachelor’s degree in Electronics Engineering from Mumbai University and has done his Master’s in Business Administration (Marketing) from University of Pune. He is currently settled in Bangalore with his wife. He enjoys travelling to new places and reading motivational books.

Textul de pe ultima copertă

Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. 

The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. 

You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways.

You will:
  • Review the new features of TensorFlow 2.0
  • Use TensorFlow 2.0 to build machine learning and deep learning models 
  • Perform sequence predictions using TensorFlow 2.0
  • Deploy TensorFlow 2.0 models with practical examples

Caracteristici

Covers the transition from TensorFlow 1.0 to TensorFlow 2.0 Covers advanced techniques such as GANs and transfer learning Covers deployment of TensorFlow 2.0 based machine learning and deep learning models to production