February 2017
Intermediate to advanced
370 pages
8h 11m
English
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Chapter 3. Linear Regression
In this chapter, we will cover the basic recipes for understanding how TensorFlow works and how to access data for this book and additional resources. We will cover the following areas:
- Using the Matrix Inverse Method
- Implementing a Decomposition Method
- Learning the TensorFlow Way of Regression
- Understanding Loss Functions in Linear Regression
- Implementing Deming Regression
- Implementing Lasso and Ridge Regression
- Implementing Elastic Net Regression
- Implementing Regression Logistic Regression
Introduction
Linear regression may be one of the most important algorithms in statistics, machine learning, and science in general. It's one of the most used algorithms and it is very important to understand how to implement it and its various ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.