Hi, I am Victor Basu. Professionally I am a Data Scientist. I love to do research in the field of Machine Learning and Deep Learning. I am familiar with computer vision, NLP and speech recognition. I have a hand full of experience with the technologies required today at the industry level. I am also a Notebooks Master at Kaggle and contributed to keras.io.
My contribution to Keras -

• Drug Molecule Generation with VAE
• An approach towards implementing a depth estimation model with a convnet.

Highlight

Facial Emotion Recognition

In this project, I have developed an end-to-end pipeline for real-time Facial emotion recognition application through full-stack development. The frontend is developed in react.js and the backend is developed in FastAPI. The emotion prediction model is built with Tensorflow Keras, and for real-time face detection with animation on the frontend, Tensorflow.js have been used.

GitHub project repository - Facial Emotion Recognition

High-quality youtube video is available at - https://youtu.be/aTe05n6T5Vo

Architecture to host QuickSight Dashboard for HuggingFace model monitoring deployed on SageMaker along with data EDA

This is a solution that demonstrates how to train and deploy a pre-trained Huggingface model on AWS SageMaker and publish an AWS QuickSight Dashboard that visualizes the model performance over the validation dataset and Exploratory Data Analysis for the pre-processed training dataset. With this as the architecture for the proposing solution, we try to solve the classification of medical transcripts through Machine Learning, which is basically solving a Bio-medical NLP problem. In this solution, we also discuss feature engineering and handling imbalanced datasets through class weights while training by writing a custom Huggingface trainer in PyTorch.

GitHub project repository - Host QuickSight Dashboard for HuggingFace model monitoring deployed on SageMaker along with data EDA
Demo video - https://youtu.be/RhTSnn41cnM