Machine Learning with TensorFlow, Second Edition
Chris A. Mattmann
  • MEAP began January 2020
  • Publication in February 2021 (estimated)
  • ISBN 9781617297717
  • 472 pages (estimated)
  • printed in black & white
free previous edition eBook included
An eBook copy of the previous edition of this book is included at no additional cost. It will be automatically added to your Manning Bookshelf within 24 hours of purchase.

Chris Mattmann has done a colossal and commendable job: I highly recommend it.

Alain Lompo
Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers.

About the Technology

TensorFlow, Google’s library for large-scale machine learning, makes powerful ML techniques easily accessible. It simplifies often-complex computations by representing them as graphs that are mapped to machines in a cluster or to the processors of a single machine. Offering a complete ecosystem for all stages and types of machine learning, TensorFlow’s end-to-end functionality empowers machine learning engineers of all skill levels to solve their problems with ML.

About the book

This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning, and how to utilize the TensorFlow library to rapidly build powerful ML models. You'll learn the basics of regression, classification, and clustering algorithms, applying them to solve real-world challenges such as call center volume prediction and sentiment analysis of movie reviews. Once you’ve mastered core ML concepts, you'll move on to the money chapters: exploring cutting-edge neural network techniques such as deep speech classifiers, facial identification, and auto-encoding with CIFAR-10. Plus, in all-new appendices you'll cover updating your code to TensorFlow 2.0, and much-needed techniques for running your code inside Docker containers. Digest this book, and you'll be able to start modelling your everyday problems as automated machine learning tasks.

What's inside

  • Matching your tasks to the right machine-learning or deep-learning approach
  • Visualizing algorithms with TensorBoard
  • Sharing your results with collaborators using other frameworks
  • Understanding and using neural networks
  • Reproducing and employing predictive science
  • Downloadable Jupyter Notebooks for all examples
  • Questions to test your knowledge
  • Examples use the super-stable 1.14.1 branch of TensorFlow
  • Techniques to reuse TensorFlow 1.0 code in TensorFlow 2.x

About the reader

Written for developers experienced with Python and algebraic concepts like vectors and matrices.

About the author

Chris Mattmann is the Deputy Chief Technology and Innovation Officer at NASA Jet Propulsion Lab, where he has been recognised as JPL's first Principal Scientist in the area of Data Science. Chris has applied TensorFlow to challenges he’s faced at NASA, including building an implementation of Google’s Show & Tell algorithm for image captioning using TensorFlow. He contributes to open source as a former Director at the Apache Software Foundation, and teaches graduate courses at USC in Content Detection and Analysis, and in Search Engines and Information Retrieval.

Nishant Shukla wrote the first edition of Machine Learning with TensorFlow.

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