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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