My primary objective is to learn and spread the power of Artificial Intelligence. The fields of Machine Learning and Blockchain are growing at a remarkable rate and I believe this is the next paradigm shift in the digital world. With the integration of AI into almost everything and the upcoming DApps, we are entering a new era of software.
I aim to help make the current technology better for people by using AI and hope to become a pioneer in the field of Machine Learning and Artificial Intelligence.
One of the things that I deeply believe in is this statement by Elon Musk - "People are mistaken when they think that technology just automatically improves. It only improves if a lot of people work very hard to make it better and actually it will, I think, by itself degrade. You look at great civilizations like Ancient Egypt, and they were able to make the pyramids and they forgot how to do that. And the Romans, they built the incredible aqueducts. They forgot how to do it."
Just trying to look at the future and not be sad about it!
Built various ETL pipelines for expansion of the proprietary “data engine”, which collects start-ups and use case data, deployed on Airflow cluster. Solved the problem of tracking the company features overtime.
Built a scalable and robust multi-tier proprietary data engine responsible for identification and collection of data on Early-Stage startups with various 3rd party API integrations. Lead the development and deployment of internal APIs and other web hook integrations. Contributed to ML research implementation and deployment. Also providing support during investment analysis & due diligence.
Detecting lightning strikes in high speed videos: Built an end to end classifier using Deep CNN Architectures using PyTorch to identify given any image is a lightning image or not. This model provides the basis for the smart data warehousing for the project.
As a Student Mentor and project reviewer, I mentored and reviewed projects of hundreds of students enrolled in Intro to Self Driving Car Nanodegree (iSDCND), Self Driving Car Nanodegree (SDCND), AI Programming with Python Nanodegree (AIPND).
Building a tool for travellers which allows them to share their experience with the world by way of Travlrr Stories. These stories are then, presented to other users when planning their trips and when looking for something to do or someone to meet when they are travelling. USP is that the interactions in the app happen in the form of stories, which can be joined by friends you travel with or meet on your trips.
Feature extraction and detection from the text in the context of recorded food deals from various websites for the purpose of analysing the nature and information about it from the data. Developed a Facebook Messenger bot using basic NLP and Decision Flow implementation. Also helped them with implementation of various API’s for the web platform.
Built an end to end classifier using pretrained Deep CNN Architectures to identify whether any given frame from the video is a lightning image or not. Also working on the analysis of corresponding Electric - Field data using LSTM architecture in order to make the current results better.
Developed and trained a Feed Forward Classifier Network on top of VGGNet to label the given image of a flower as one of the 102 categories available in the 102 Category Flower Dataset. Trained the model to get an accuracy of ~72%. Currently working on getting state of the art results using ResNet.
Developed the project following the description of the Deep Q Learning algorithm (Deep Mind) described in the Playing Atari games with Deep Reinforcement Learning. This project shows that this learning algorithm can further generalized to the notorious Flappy Bird.
Developed a Fully Convolution Network on top of VGGNet using skip layer architecture to label the pixels of a road in a image. This project can be considered as an extension of Jonathan Long and Evan Shelhamer's work in the paper "Fully Convolution Network for Semantic Segmentation". The model was trained on Kitti road data set on an AWS GPU Machine.
Developed an android application which allows a user to learn 5 new words of English language every day through interactive activities like quiz based games and activities like fill in the blanks and match the following.
Feature extraction and detection from the text in the context of recorded food deals from various websites for the purpose of analyzing the nature and information about it from the data. Further, the pipeline was connected to an EC2 instance on the cloud with seamless integration to the existing product.
P.S. - I have also done a lot of other cool stuff. For details about that you can either have a look at my CV, additional info or github.
BTCS-101 Fundamentals of Comouter Programming & IT
BTCS-301 Computer Architecture
BTCS-304 Data Strcutures
BTCS-305 Object Oriented Programming
BTCS-401 Operating Systems
BTCS-403 Computer Networks
BTCS-405 System Programming
BTCS-501 Computer Networks - II (AdHoc Networks & Wireless Communications)
BTCS-503 Design & Analysis of Algorithms
BTCS-504 Computer Graphics
BTCS-603 Software Engineering
BTCS-702 Theory of Computation
BTCS-901 Web Technologies
BTCS-502 Relational Database Management Systems
BTCS-602 Distributed Database Management systems
BTCS-701 Artificial Intelligence
CS-229 Machine Learning
CS-231n Convolutional Neural Networks for Visual Recognition
Udacity's AI Programming with Python Nanodegree
FAST.AI's Practical Deep Learning For Coders
Big Data Analytics with Spark
BTAM-101 Mathematics - I
BTAM-101 Mathematics - II
BTAM-101 Mathematics - III
BTCS-402 Discrete Structures
BTCS-607 Probability, Statistics and Random Process