Squeeze and Excitation Networks
Convolutional Neural Network (CNN) has been most widely used in the field of computer vision and visual perception to solve multiple tasks such as image classification, semantic…
Read moreWhat is Residual Network or ResNet?
Deep neural networks have become popular due to their high performance in real-world applications, such as image classification, speech recognition, machine translation and many more. Over time…
Read moreSupervised vs Unsupervised Learning
In this article, we are going to explore the two machine learning approaches – supervised and unsupervised learning. It is one of the most basic questions for…
Read moreWhat is Transfer Learning? – A Simple Introduction.
Transfer Learning is a technique in machine learning where we reuse a pre-trained model to solve a different but related problem. It is one of the popular…
Read moreUNET Implementation in PyTorch
This tutorial focus on the implementation of the image segmentation architecture called UNET in the PyTorch framework. It’s a simple encoder-decoder architecture developed by Olaf Ronneberger et…
Read moreConvolution Neural Network (CNN) – Fundamental of Deep Learning
Convolutional Neural Network (CNN) is used to solve a wide range of visual tasks such as image classification, object detection, semantic segmentation, and many more. CNN consists…
Read moreUNET Implementation in TensorFlow using Keras API
In this post, you will learn how to implement UNET architecture in TensorFlow using Keras API. The post helps you to learn about UNET, and how to…
Read moreWhy do we need GPU for Deep Learning?
The current era started to move towards Artificial Intelligence, which massively impacted the world with its ability to achieve the tasks that were a dream of humanity….
Read moreWhat is RESUNET
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…
Read moreWhat is UNET?
UNET is an architecture developed by Olaf Ronneberger and his team at the University of Freiburg in 2015 for biomedical image segmentation. It is a highly popular…
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