RESUNET refers to Deep Residual UNET. It’s an encoder-decoder architecture developed by Zhengxin Zhang et al. for semantic segmentation. It was initially used for the road extraction from the high-resolution aerial images in the field of remote sensing image analysis. Later, it was adopted by researchers for multiple other applications […]

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What is UNET?

UNET is an architecture developed by Olaf Ronneberger et al. for Biomedical Image Segmentation in 2015 at the University of Freiburg, Germany. It is one of the most popularly used approaches in any semantic segmentation task today. It is a fully convolutional neural network that is designed to learn from […]

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GAN – What is Generative Adversarial Network?

Generative Adversarial Network or GAN is a machine learning approach used for generative modelling designed by Ian Goodfellow and his colleagues in 2014. It is made of two neural networks: generator network and a discriminator network. The generator network learns to generate new examples, while the discriminator network tries to […]

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Scrap Images in Python

An Introduction to Scraping Images With Python

The Internet is a rich source of data and information in the world that is easy to acquire. This data includes images, PDF, text, audio, and video. To acquire the data it is necessary to scrape it. In this tutorial, we are going to learn about scraping images with python […]

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OpenAI GPT-3: The successor of OpenAI GPT-2

The research lab OpenAI has released a preprint arXiv paper, titled “Language Models are Few-Shot Learners” or OpenAI GPT-3, which is a continuation of their previous work entitled “Language Models are Unsupervised Multitask Learners” or GPT-2. As a recap. GPT-2 is a language model based on the transformer architecture with […]

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Introduction to autoencoder

Introduction to Autoencoders

In today’s article, we are going to discuss a neural network architecture called autoencoders. This article is aimed at Machine Learning and Deep Learning beginners who are interested in getting a brief understanding of the underlying concepts behind autoencoders.  So let’s dive in and get familiar with the concept of […]

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