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TensorFlow Machine Learning Projects

You're reading from   TensorFlow Machine Learning Projects Build 13 real-world projects with advanced numerical computations using the Python ecosystem

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789132212
Length 322 pages
Edition 1st Edition
Languages
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Authors (2):
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Ankit Jain Ankit Jain
Author Profile Icon Ankit Jain
Ankit Jain
Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
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Table of Contents (17) Chapters Close

Preface 1. Overview of TensorFlow and Machine Learning FREE CHAPTER 2. Using Machine Learning to Detect Exoplanets in Outer Space 3. Sentiment Analysis in Your Browser Using TensorFlow.js 4. Digit Classification Using TensorFlow Lite 5. Speech to Text and Topic Extraction Using NLP 6. Predicting Stock Prices using Gaussian Process Regression 7. Credit Card Fraud Detection using Autoencoders 8. Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks 9. Generating Matching Shoe Bags from Shoe Images Using DiscoGANs 10. Classifying Clothing Images using Capsule Networks 11. Making Quality Product Recommendations Using TensorFlow 12. Object Detection at a Large Scale with TensorFlow 13. Generating Book Scripts Using LSTMs 14. Playing Pacman Using Deep Reinforcement Learning 15. What is Next? 16. Other Books You May Enjoy

What is TensorFlow Lite?

Before we take a deep dive into TensorFlow Lite, let's try to understand what are the advantages of doing ML on edge devices like mobile/tablet and others.

  • Privacy: If inference on a ML model can be performed on a device, user data doesn't need to leave the device, which helps in preserving the privacy of the user.
  • Offline predictions: The device doesn't need to be connected to a network to make predictions on a ML model. This unlocks a lot of use cases in developing nations such as India where network connectivity is not so great.
  • Smart devices: This can also enable the development of smart home devices such as microwaves and thermostats with on-device intelligence.
  • Power efficient: An on-device ML can be more power-efficient as there is no need to transfer data back and forth to the server.
  • Sensor data utilization: ...
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