Machine Learning with TensorFlow, Second Edition

Machine Learning with TensorFlow, Second Edition
PDF
  • eBook:
    Machine Learning with TensorFlow, 2nd Edition
  • Author:
    Mattmann A. Chris
  • Edition:
    2 edition
  • Categories:
  • Data:
    February 2, 2021
  • ISBN:
    1617297712
  • ISBN-13:
    9781617297717
  • Language:
    English
  • Pages:
    456 pages
  • Format:
    PDF

Book Description
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. 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
Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need.

About the book
Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10.

What's inside

    Machine Learning with TensorFlow
    Choosing the best ML approaches
    Visualizing algorithms with TensorBoard
    Sharing results with collaborators
    Running models in Docker

Content

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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