Building a Logistic Regression Classifier in PyTorch
Logistic regression is a type of regression that predicts the probability of an event. It is used for classification problems and has many applications in the fields of machine learning, artificial intelligence, and data mining.
The formula of logistic regression is to apply a sigmoid function to the output of a linear function. This article discusses how you can build a logistic regression classifier. While previously you have been working on a single-varable dataset, here we’ll be using a popular MNIST dataset to train and test our model. After going through this article, you’ll learn:
- How to use logistic regression in PyTorch and how it can be applied to real-world problems.
- How to load and analyze torchvision datasets.
- How