In this tutorial, we are going to focus on reading an image using the Python programming language. For this, we are going to use the OpenCV library. OpenCV refers to Open Source Computer Vision library aimed at computer vision and machine learning. To use OpenCV in Python install the following […]
Read MoreThis 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 al. for Biomedical Image Segmentation in 2015 at the University of Freiburg, Germany. What is Image Segmentation? An image consists of multiple objects […]
Read MoreRESUNET 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 […]
Read MoreUNET 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 […]
Read MoreIn this tutorial, we are going to implement a Deep Convolutional Generative Adversarial Network (DCGAN) on Anime faces dataset. The code is written in TensorFlow 2.2 and Python3.8 . According to Yann LeCun, the director of Facebook AI, GAN is the “most interesting idea in the last 10 years of […]
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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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We are going to continue our journey on the autoencoders. In this article, we are going to build a convolutional autoencoder using the convolutional neural network (CNN) in TensorFlow 2.0. Let us first revise, what are autoencoders? Autoencoders are neural networks that attempt to mimic its input as closely as […]
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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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Recent advancement in the field of deep learning has enabled us to develop models that yield impressive results across various fields, from image classification, object detection, to speech recognition. However, developing the architecture for each problem is a challenge in itself. In this paper, the authors present a single deep […]
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Data augmentation is a process that enables you to increase the amount of training data by making reasonable modifications in your existing data. It helps you to increase the diversity of your training data which is essential for developing a robust model. This then, generally speaking, improves the performance of […]
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