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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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Toc

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

Detecting exoplanets in outer space

For the project explained in this chapter, we use the Kepler labeled time series data from Kaggle: https://www.kaggle.com/keplersmachines/kepler-labelled-time-series-data/home. This dataset is derived mainly from the Campaign 3 observations of the mission by NASA's Kepler space telescope.

In the dataset, column 1 values are the labels and columns 2 to 3198 values are the flux values over time. The training set has 5087 data points, 37 confirmed exoplanets, and 5050 non-exoplanet stars. The test set has 570 data points, 5 confirmed exoplanets, and 565 non-exoplanet stars.

We will carry out the following steps to download, and then preprocess our data to create the train and test datasets: 

  1. Download the dataset using the Kaggle API. The following code will be used for the same:
armando@librenix:~/datasets/kaggle...
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