#### 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.
Preface
Free Chapter
Getting Started with TensorFlow
The TensorFlow Way
Support Vector Machines
Nearest-Neighbor Methods
Natural Language Processing
Convolutional Neural Networks
Recurrent Neural Networks
Taking TensorFlow to Production
More with TensorFlow
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# Introduction

SVMs are a method of binary classification. The basic idea is to find a linear separating line in two dimensions (or hyperplane for more dimensions) between the two classes. We first assume that the binary class targets are -1 or 1, instead of the prior 0 or 1 targets. Since there may be many lines that separate two classes, we define the best linear separator that maximizes the distance between both classes:

Figure 1

Given two separable classes, o and x, we wish to find the equation for the linear separator between the two. The left-hand graph shows that there are many lines that separate the two classes. The right-hand graph shows the unique maximum margin line. The margin width is given by . This line is found by minimizing the L2 norm of A.

We can write such a hyperplane as follows:

Here, A is a vector of our partial slopes and x is a vector of inputs. The width...