“Why is the focus of Deep Learning shifting towards Graph Neural Networks?”, I thought to myself. Is it because of the data structure or efficient computation? It all begins with the structure of graphs and how they are able to solve relational problems and support distributed computing. Evolution really takes place when the same structure is applied to the state of the art Machine Learning Algorithms. From implementation in simple Matrix Factorization and linear regression algorithms to Graph Neural Networks, we can observe the evolution in technology.

In this article, we will go through graphs, their advantages, and how they…

Every time our surroundings change, technology evolves with it. The increased video conferencing during COVID has highlighted the concept of Virtual Backgrounds. Name any video communication service, be it Google Meet, Zoom, or MS Teams and you will notice this feature. Protecting one’s privacy or simply hiding their chaotic surroundings, Virtual backgrounds can really be helpful.

Curious about how you can implement it with simple coding? This article contains all information needed to understand and execute it in your own system. Concepts of Image Segmentation and masking are explained with step-by-step coding.

Virtual Background

The aim of a virtual background is to…

Pose Estimation AI(PostNet) is becoming more widely accepted and used in our daily lives, starting from Fitness Applications to Research projects. They hold the power to replace so much of the capital spent on buying hardware and other electronics used for the same task. Let's take the example of games, to clone the movement of a real-life sportsperson into a game, heavy equipment is required. I believe that in the future the same task could be efficiently performed by these neural networks.

In this article, we will understand some basic concepts that lead up to classifying movement. The topics of…

Genetic algorithms are a part of evolutionary algorithms used for searching and optimization problems. They are an algorithm inspired by the evolution happening naturally and work on the motto “Survival of the Fittest”. This article will cover some basic implementation of this algorithm but more importantly a wider view of its potential and applications.

Introduction

Every person wanting to work on deep learning starts with the MNIST (Modified National Institute of Standards and Technology database) handwritten data classification. It is a fairly simple code of preprocessing and training, and no matter how good or bad your model is the accuracy crosses 0.99 almost every time. So we will be using a genetic algorithm with CNN to classify the MNIST dataset.

But to all those deep learning enthusiasts interested in learning new ways of training your model and increasing its efficiency here is something. I have been going through the genetic algorithm and its amazing. It…