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)

Computing with mixed distance functions

When dealing with data observations that have multiple features, we should be aware that features can be scaled differently, on different scales. In this recipe, we will account for that to improve our housing value predictions.

Getting ready

It is important to extend the nearest-neighbor algorithm, to take into account variables that are scaled differently. In this example, we will illustrate how to scale the distance function for different variables. Specifically, we will scale the distance function as a function of the feature variance.

The key to weighting the distance function is to use a weight matrix. The distance function, written with matrix operations, becomes the following...