Machine Learning with TensorFlow

Machine Learning with TensorFlow

by Mattmann, Chris A.

Type Book
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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. Summary 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.

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Title Statements

  • Full title :Machine Learning with TensorFlow / Chris Mattmann ; foreword by Scott Penberthy.

Summary

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. Summary 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.

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Edition Statements

  • Publication Date:2020
  • Edition:Second edition

Publication Statements

Notes

Includes index.

The Physical Object

  • Description:1 online resource (xxix, 420 pages :) : illustrations.

Edition Identifiers

  • ISBN:9781638350866, 1638350868
  • oclc:on1293641646
  • LC Classification:Q325.5
  • Dewey Decimal:006.31

Table of Contents

Part 1. Your machine-learning rig. 1. A machine-learning odyssey -- 2. TensorFlow essentials -- Part 2. Core learning algorithms. 3. Linear regression and beyond -- 4. Using regression for call-center volume prediction -- 5. A gentle introduction to classification -- 6. Sentiment classification : large movie-review dataset -- 7. Automatically clustering data -- 8. Inferring user activity from Android accelerometer data -- 9. Hidden Markov models -- 10. Part-of-speech tagging and word-sense disambiguation -- Part 3. The neural network paradigm. 11. A peek into autoencoders -- 12. Applying autoencoders: the CIFAR-10 image dataset -- 13. Reinforcement learning -- 14. Convolutional neural networks -- 15. Building a real-world CNN : VGG-Face and VGG-Face Lite -- 16. Recurrent neural networks -- 17. LSTMs and automatic speech recognition -- 18. Sequence-to-sequence models for chatbots -- 19. Utility landscape.

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