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)

Working with kernels in TensorFlow

The prior SVMs worked with linear separable data. If we separate non-linear data, we can change how we project the linear separator onto the data. This is done by changing the kernel in the SVM loss function. In this chapter, we introduce how to change kernels and separate non-linear separable data.

Getting ready

In this recipe, we will motivate the usage of kernels in Support Vector Machines. In the linear SVM section, we solved the soft margin with a specific loss function. A different approach to this method is to solve what is called the dual of the optimization problem. It can be shown that the dual for the linear SVM problem is given by the following formula:

To this, the following...