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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

Google Speech Commands Dataset

The Google Speech Commands Dataset was created by the TensorFlow and AIY teams to showcase the speech recognition example using the TensorFlow API. The dataset has 65,000 clips of one-second-long duration. Each clip contains one of the 30 different words spoken by thousands of different subjects. 

The Google Speech Commands Dataset is available from the following link: http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz.

The clips were recorded in realistic environments with phones and laptops. The 35 words contained noise words and the ten command words most useful in a robotics environment, and are listed as follows:

  • Yes
  • No
  • Up
  • Down
  • Left
  • Right
  • On
  • Off
  • Stop
  • Go

More details on how the speech dataset is prepared can be found in the following links:

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