Book Image

TensorFlow Machine Learning Projects

By : Ankit Jain, Amita Kapoor
Book Image

TensorFlow Machine Learning Projects

By: Ankit Jain, Amita Kapoor

Overview of this book

TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

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-kepler$ kaggle datasets download -d keplersmachines/kepler-labelled-time-series-data

Downloading kepler-labelled-time-series-data.zip to /mnt/disk1tb...