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Hey There, I'm Viswas!

Aspiring Cloud Architect and WebApp Developer.

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About

Coder.
Innovator.
Dreamer.

An Associate Software Developer with expertise in Java and Spring Boot, focused on creating robust web applications, and enhance business reach.

Striving to become a Cloud Architect who can create and develop innovative solutions to real-world business problems and drive projects.

Aside from my technical background, I take pride in my strong collaborative abilities and passion for continuous learning. I maintain work-life balance through chess and exploring international cuisines, which have enhanced my strategic thinking and adaptability.

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Expertise

What I Deliver.

I specialize in designing and executing solutions that tackle business challenges in a dynamic and competitive environment.

Dynamic Web Projects

I use Java, SpringBoot, JavaScript, and HTML/CSS to develop dynamic and scalable web applications tailored to business needs and optimized for performance.

ML & AI Solutions

With a strong background in AI, I’ve developed models for image and audio enhancement, leveraging machine learning to solve complex challenges in these fields.

Cloud Architecture

As an aspiring cloud architect, I bring theoretical knowledge of cloud infrastructure, focusing on scalability, security, and cost-efficiency for future projects.

Innovation and Drive

I am passionate about innovation, always seeking fresh solutions and creative approaches to help businesses stay ahead in an ever-evolving market.

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Professional Experience

Teams and Organizations
that Made Me

Associate Software Developer

2024 - Present

Developed Dynamic Web Projects as Team.

Technologies

My Tech Stack

I have hands-on experience with a wide range of technologies that help me develop scalable, efficient, and cutting-edge solutions.

Java
SpringBoot
JavaScript
HTML & CSS
Python
AWS
TensorFlow
Git
C
C++
Mongo
sql
mysql
keras
PyTorch
react

Latest additions to my portfolio.

Project Link

Music Genre classification using Ensemble Machine Learning

This project focuses on music genre classification using an ensemble machine learning model that combines Artificial Neural Networks and Convolutional Neural Networks. By extracting MFCC features from audio samples, it enhances classification accuracy. The approach benefits music streaming, recommendation systems, and automated cataloging, improving user experiences and music analysis efficiency.

Project Link

Blind Denoising Autoencoders for Image Restoration

Developed a method to enhance and reconstruct images using a Blind Denoising Autoencoder setup. The model is completely Unsupervised and does not a single reference image post training. The model performs exceedingly well for the purposes of Character reconstruction in particular which was tested on applications like License plate reconstruction. The model also showed very good results at denoising blurry underwater imagery.

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Click below to get in touch, I am always looking out for new and inquisitive opportunities!

Let's Work Together!