Midlands State University Library
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Machine learning with tensorflow / created by Chris Mattmann ; foreword by Scott Penberthy.

By: Contributor(s): Material type: TextTextPublisher: Manning Publications Company, 2020Copyright date: ©2020Edition: Second editionDescription: xxix, 420 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781617297717
  • 9781617297717
Subject(s): LOC classification:
  • Q325.5 MAT
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.
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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Includes index.

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.

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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