Computer Vision · Optical Character Recognition · Pattern Recognition

Convolutional Neural Networks (CNNs): An Illustrated Explanation – XRDSXRDS

Artificial Neural Networks (ANNs) are used everyday for tackling a broad spectrum of prediction and classification problems, and for scaling up applications which would otherwise require intractable amounts of data. ML has been witnessing a “Neural Revolution”1 since the mid 2000s, … Continue reading → Source: Convolutional Neural Networks (CNNs): An Illustrated Explanation – XRDSXRDS

Computer Vision · Digital Image Processing · Machine Learning

How to become an expert in Computer Vision?

The question has been puzzling me for quite a while; How to become an expert in Computer Vision? And by expert I mean as a software engineer aka developer. Should I read more books? enroll in an academic program? online courses? contribute to open source projects? apply for a related internship / job? implement research papers?… Continue reading How to become an expert in Computer Vision?

Pattern Recognition

A Multi Independent Feature Bayesian Classifier

In a previous post, a single feature Bayesian classifier was implemented. In order to generalize our classifier to use more than one feature, let us introduce the concept of Bayes Risk. Allowing the use of more than one feature merely requires replacing the scalar x by the feature vector X, where x is in a… Continue reading A Multi Independent Feature Bayesian Classifier

Pattern Recognition

A Single Feature Bayesian Classifier

Bayesian decision theory is a fundamental statistical approach to the problem of pattern classification. Bayesian formula can be expressed in English by saying that posterior = likelihood * prior / evidence Notice that it is the product of the likelihood and the prior probability that is most important in determining the posterior probability. The evidence… Continue reading A Single Feature Bayesian Classifier