I am Himadri Sankar Chatterjee. Systems Enginner at TCS. Previously, intern at BNP Paribas and ConvAI. I did my MCA from Vellore Institute of Technology and completed my Bachelor's in Computer Science from RKMRC. I have been enthusiastic about the field Artificial Intelligence specifically in the field of Deep Learning.
Apart from that I have actively participated in many online coding contests through CodeChef, HackerRank. I have also tried to explore the field of practical application of Deep Learning through small projects.
Trained with Azure Platform technologies, worked on developing cloud-based infrastructure solutions for various customer requirements.
Full stack development of banking application for internal invoice management. Extended functionalities of the application with enhanced UI built using Angular and optimized backend operations developed with C# and SQL Server
Worked on development of the main site and other services for the startup. Managed and built the complete frontend in ReactJS and implemented backend in Python and Flask with hosting in GCP.
It was a work-from-home internship on technical content writing based on technological advancements.
A real-time Covid-19 Tracker. Built with React JS, it has a responsive design and updated stats. Powered by Firebase Hosting
A responsive and sleek design of the probable new Hulu UI built using React JS. Added functionality to top movies based on genre. Hosted online through Firebase.
A responsive and elegant mockup clone of the official Airbnb app with minimilastic design, built using React JS, with Material UI and made available online with Firebase Hosting.
A clone of the Tinder app built using the MERN stack. The frontend is hosted in Firebase.
It was originally an idea from an seminar discussion, that was originally discontinued due to unavailability of enough computation resources. I took the responsibility to atleast achieve some result in this matter. I trained a C3D model on the ViolentFlow dataset to build an autonomous violent behabiour detector. The model has an accuracy of ~62%
This is the basic project for our final year of bachelor's degree, focused on understanding various Convolutional Neural Network and implementing the most suitable one.
We try to implement a car that will learn to drive through various terrains to reach its destination. The environment is designed in Unity and OpenAI gym library is used to train a DeepQ Network to drive the car.
Using a series of Rest APIs to post user-submitted reviews on social media platform and using an online hosted AI-powered classifier to suggest relevant review hashtags.
Trained a convolutional neural network to predict the possible genres that the movie spans based on the circulated posters of the movie, with consistent accuracy.
Following the course on Deep Learning in Coursera, the concept of Convolutional Neural Network intrigued me. This project helped me in understanding the concepts. **Its not well documented for reproduction**
This was the first project I completed after completing the Machine Learning Crash Course, by the Google Developer's Group.
This project utilizes the BeautifulSoup web-scraping library in Python to extract information on the top 1000 movies from the IMdB website. Some initial data analysis was performed on the data.
A simple implementation of Generative Adversarial Network to generate Anime Faces, training from dataset available in kaggle.
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