11. PyTorch Tutorial---Linear Regression

In this chapter, we will focus on basic examples of linear regression using TensorFlow. Logistic regression or linear regression is a supervised machine learning method used to classify ordered discrete categories. The goal of this chapter is to enable the user to predict the relationship between a predictor variable and one or more independent variables by building a model.

The relationship between these two variables is considered to be linear, i.e. if y is the dependent variable and x is considered as the independent variable, then the linear regression relationship between the two variables is as shown in the following equation:

Y = Ax + b

Next, we will design an algorithm for linear regression so that we can understand the two important concepts mentioned below:

1. Cost function
2. Gradient descent algorithm

The schematic diagram of linear regression is as follows:

Interpret the result
Y = ax + b
where
- the value of a is the slope.
- The value of b is the y-intercept.
- r is the correlation coefficient.
- r2 is the correlation coefficient.

A graphical view of the linear regression equation looks like this:

The steps to implement linear regression using PyTorch are as follows:

step 1

Import the necessary packages to create linear regression in PyTorch, using the following code:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import seaborn as sns
import pandas as pd
%matplotlib inline

sns.set_style(style='white