In this chapter, we will cover recipes involving linear regression. We start with the mathematical formulation for solving linear regression with matrices and move on to implementing standard linear regression and variants with the TensorFlow paradigm. 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 logistic regression
