Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By : Nick McClure
Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By: Nick McClure

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production. By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.
Table of Contents (13 chapters)

Implementing a multi-class SVM

We can also use SVMs to categorize multiple classes instead of just two. In this recipe, we will use a multi-class SVM to categorize the three types of flowers in the iris dataset.

Getting ready

By design, SVM algorithms are binary classifiers. However, there are a few strategies employed to get them to work on multiple classes. The two main strategies are called One versus all, and One versus one.

One versus one is a strategy where a binary classifier is created for each possible pair of classes. Then, a prediction is made for a point for the class that has the most votes. This can be computationally hard, as we must create classifiers for k classes.

Another way to implement multi-class classifiers...