Learn Tensorflow 2.0 - by Pramod Singh & Avinash Manure (Paperback)
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<p/><br></br><p><b> Book Synopsis </b></p></br></br>Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. <br>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. <br>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.<br><b>What You'll Learn</b><ul><li>Review the new features of TensorFlow 2.0</li><li>Use TensorFlow 2.0 to build machine learning and deep learning models </li><li>Perform sequence predictions using TensorFlow 2.0</li><li>Deploy TensorFlow 2.0 models with practical examples</li></ul><b><br></b><b>Who This Book Is For</b><br><b><br></b>Data scientists, machine and deep learning engineers. <p/><p/><br></br><p><b> From the Back Cover </b></p></br></br>Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. <br>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. <br>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.<br>You will: <ul><li>Review the new features of TensorFlow 2.0</li><li>Use TensorFlow 2.0 to build machine learning and deep learning models </li><li>Perform sequence predictions using TensorFlow 2.0</li><li>Deploy TensorFlow 2.0 models with practical examples</li></ul><br><p/><br></br><p><b> About the Author </b></p></br></br>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.<p></p><br>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.<p></p> 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.<br>
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