ABOUT

Sup,

I am a Machine Learning, and Artificial Intelligence enthusiast, full stack web developer, and a nascent native applications developer currently pursuing my Bachelors in Computer Science at Vellore Institute of Technology. Currently focusing on Deep Learning for Computer Vision and looking for a long-term research-based internship.

Love to solve

real-world problems using Deep Learning and Machine Learning. I am continually trying new ways to integrate my machine learning models to the web. Moreover, I am always on the learning path to grasp everything that interests me.

Working on

Image Fusion using CNN and Deep Learning. Recently finished developing a Python package which extracts various content-based features from grayscale images which can be further used for classification.

I write

programming blogs related to deep learning, computer vision, python, machine learning, web scraping, and web development. My primary motivation is to help others like me through these blogs of mine.

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BLOGS

Multi-class Image classification using CNN over PyTorch, and the basics of CNNNEW

I always had this conundrum about how an image goes through a Convolutional Neural Network; in this blog, I try to address it and build a multi-class classifier for classifying apparels from scratch using PyTorch. And no it's not on the MNIST Fashion dataset. It also shows how to work with DataLoaders in PyTorch and many other things. Please have a read and comment down if there are any discrepancies.

Feature extraction from Medical Images and an introduction to “xtract-features.”

With the advent of neural networks and advancements in Deep Learning for Computer Vision, we have stopped thinking about the features that are being extracted through these black box models and how they affect the accuracy of the model. And with the recent advancements in Transfer Learning, we have also neglected the model building process. We are taking a pre-trained model and change the last layers by adding our layers based on the number of classifications required to be predicted for our data.

Working with Firebase Real-Time database using ReactJS and UIKit and finally launching the web-app to Netlify.

Before starting with this react + firebase project you first need to setup your machine, install NODE and NPM. Follow this article.After completing the setup, you need to install create-react-app package, open the terminal and type in the following,

Web Scraping using Python and BeautifulSoup.

Recently, I had to gather some textual data for my Natural Language Processing project for rating restaurants based on their food, ambience, service and other important factors. You would think, “ooh, its like all the other NLP related projects were sentiment analysis is performed on the comments and a score is generated”, yes its the same but there is a catch, I won’t be using the comments of the common public but the reviews and ratings by some food bloggers.

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CONTACT

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