Book Image

TensorFlow Machine Learning Cookbook. - Second Edition

By : Sujit Pal, Nick McClure
Book Image

TensorFlow Machine Learning Cookbook. - Second Edition

By: Sujit Pal, 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 deming regression

In this recipe, we will implement deming regression, which means we will need a different way to measure the distance between the model line and data points.

Deming regression goes by several names. It is also known as total regression, orthogonal distance regression (ODR), and shortest-distance regression.

Getting ready

If least squares linear regression minimizes the vertical distance to the line, deming regression minimizes the total distance to the line. This type of regression minimizes the error in both y and x values.

See the following diagram for a comparison:

Figure 8: Difference between regular linear regression and deming regression; linear regression on the left minimizes the vertical...