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

What is a decision tree?


Decision trees are a family of non-parametric supervised learning methods. In the decision tree algorithm, we start with the complete dataset and split it into two partitions based on a simple rule. The splitting continues until a specified criterion is met. The nodes at which the split is made are called interior nodes and the final endpoints are called terminal or leaf nodes.

As an example, let us look at the following tree:

Here, we are assuming that the exoplanet data has only two properties: flux.1 and flux.2. First, we make a decision if flux.1 > 400 and then divide the data into two partitions. Then we divide the data again based on flux.2 feature, and that division decides whether the planet is an exoplanet or not. How did we decide that condition flux.1 > 400? We did not. This was just to demonstrate a decision tree. During the training phase, that's what the model learns – the parameters of conditions that divide the data into partitions.

For classification...