Published on March 30, 2022
In Deep Tech

A beginner’s guide to Bayesian CNN

Applying bayesian on neural networks is a method of controlling overfitting. We can also apply bayesian on CNN to reduce the overfitting and we can call CNN with applied Bayesian as a BayesianCNN.

By Yugesh Verma

Convolutional neural networks(CNN) are the best way to deal with computer vision problems like image classification, object localization and detection, and image segmentation. The main reason behind this capability is CNN can easily deal with a set of non-linear data points. Most of the time we find these datasets small in amount for training CNN and standard CNN requires large size data to overcome the problem of overfitting. Bayesian CNN is a variant of CNN that can reduce the chances of overfitting while training on small-size data. In this article, we are going to discuss Bayesian CNN. The major points to be discussed in the article are listed below. Table of contents What are Bayesian neural networks?Problem with CNNWhat is Bayesian CNN?The architecture of Bayesian CNNHow Does Bayesian CNN Work?Applications of Bayesian CNN Let's first understand how Bayesian is used in a neural network. What are Bayesian neural networks? We can think of the Bayesian neur

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Yugesh Verma

Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.