Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By : Nick McClure, Sujit Pal
Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By: Nick McClure, Sujit Pal

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)

Using an address matching example

Now that we have measured numerical and text distances, we will spend some time learning how to combine them to measure distances between observations that have both text and numerical features.

Getting ready

Nearest-neighbor is a great algorithm to use for address matching. Address matching is a type of record matching in which we have addresses in multiple datasets and would like to match them up. In address matching, we may have typos in the address, different cities, or different ZIP Codes, but they may all refer to the same address. Using the nearest-neighbor algorithm across the numerical and character components of an address may help us to identify addresses that are actually the same...