Activation Function – Basics of Deep Learning

Deep learning is a subset of machine learning that utilizes neural networks with multiple layers to analyze and identify patterns in data. One of the key components…

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MobileViT architecture

What is MobileViT?

This article covers an overall summary of the MobileViT: Light-Weight, General-Purpose, and Mobile-Friendly Vision Transformers research paper. MobileViT is a lightweight and general-purpose vision transformer for mobile…

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Vision Transformer

Vision Transformer – An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale

In this blog post, we are going to learn about the Vision Transformer (ViT). It is a pure Transformer based architecture used for image classification tasks. Vision…

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MODNet Architecture

MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition

In this work, we present a lightweight matting objective decomposition network (MODNet) for portrait matting in real-time with a single input image. MODNet inputs a single RGB…

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A human like robot

Why Deep Learning is not Artificial General Intelligence (AGI)

With the development in the field of deep learning, it has become a frontier in solving multiple challenging problems in computer vision, games, self-driving cars and many…

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Deep Learning based Background Removal from Images using TensorFlow and Python

In this tutorial, we are going to learn how to use deep learning to remove background from images with TensorFlow. In short, we’ll use DeepLabV3+, a semantic…

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Implementing Custom layer in TensorFlow

Custom Layer in TensorFlow using Keras API

The majority of the people interested in deep learning must have used the TensorFlow library. It is the most popular and widely used deep learning framework. We…

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VGG16 UNET implementation in TensorFlow

VGG16 UNET Implementation in TensorFlow

In this article, we are going to implement the most widely used image segmentation architecture called UNET. We are going to replace the UNET encoder with the…

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Squeeze & Excitation Network

Squeeze and Excitation Implementation in TensorFlow and PyTorch

The Squeeze and Excitation network is a channel-wise attention mechanism that is used to improve the overall performance of the network. In today’s article, we are going…

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Semi-supervised learning

Semi-supervised Learning – Fundamentals of Deep Learning

Semi-supervised learning is a type of machine learning where we use a combination of a large amount of unlabelled data and a small amount of labelled data…

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