TensorFlow is a powerful library for machine learning that allows for the easy implementation of various algorithms, including linear regression. In this tutorial, we will be using TensorFlow tape gradient to implement a linear regression model and plot the loss graph and x and y on matplotlib. First, we will...
ChatGPT is a state-of-the-art natural language processing model developed by OpenAI. It is based on transformer architecture and is trained on a massive amount of conversational data. In this blog post, we will take a closer look at what ChatGPT is, how it works, and its applications in the field...
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 of deep learning is the activation function, which is responsible for determining the output of each neuron in the network. In this blog...
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 vision tasks. It combines the strength of the standard CNN (Convolutional Neural Network) and the Vision Transformers. It has outperformed several CNNs and...
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 Transformer (ViT) has the ability to replace the standard CNNs while achieving excellent results. The Vision Transformer (ViT) attains excellent results when pre-trained...
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 image and applies explicit constraints to solve matting sub-objectives simultaneously in one stage. The research paper is accepted at AAAI 2022 conference. Research...
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 more. Deep learning has even achieved superhuman performance in some tasks, but still, it lacks some fundamental features which are required for a...
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 segmentation based model to extract the background and foreground mask from the image. We are going to use these masks to extract the...
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 have used the different layers provided by the tf.keras API to build different types of deep neural networks. But, there are many times...
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 VGG16 implementation from the TensorFlow library. The UNET encoder would learn the features from scratch, while the VGG16 is already trained on the...
Hi Nikhil, Thanks for your dedications and explored very well. it is great helps to me. by Palani
ImportError: cannot import name 'load_data' from 'data' (C:\Users\Aravinda\anaconda3\lib\site-packages\data\__init__.py)
sir can you share a code of using squeeze and excitation network on custom CNN for a classification
Hey Nikhil, Very nice, to-the-point article on transfer learning. For a small size of dataset, an image augmentation along with…
Truly good blog short article and also valuable.