I worked on this project to compete in The Metis Challenge: Naive Bees Classifier competition. The goal of the competition is to determine the genus of the bee - Apis (honey bee) or Bombus (bumble bee) based on photographs of the insects.
I implemented a Convolutional Neural Network (CNN) architecture using Theano, trained it to extract features and determine genus of the bee based on the photographs. Before training, data was preprocessed by resizing, translating, rotating and flipping every image. This additional training data helped reduce overfitting and improved the model invariance. With this architecture and preprocessing, I achieved an accuracy of 81.9% with just 25 iterations of training on CPU.Code
The goal of this project is to build a question answering system that uses alignment over semantic roles to answer and pass a 4th grade science exam.
The baseline solution uses word overlap between question and answer sentences to predict the probable answer. In this baseline method, scoring is done by dividing number of common tokens between question and answer sentences with number of tokens in the question sentence. This system answered 48% of the questions. I tried the following two methods in order to improve upon the baseline method.
The goal of this project is to build machine learning models to predict the winners of 2015 Super Bowl and the College Football Championship using historical data.
We have predicted the outcome of football matches entirely using the knowledge of previous game statistics. We have used three different models to do this:
More information about the project can be found here.Code Report
The goal of this project is to predict reviews' star ratings on Yelp from review text.
We have built the following models that perform text analysis on review data to predict the rating stars.
We have achieved an accuracy of 61% in predicting review rating stars.Code Report Presentation
Aim of this project is, for every image with a face of a human, rate the beauty of the face, using a Convolutional Neural Network (CNN). This is done without considering any landmark features. Thus, detecting the features, training them, as well as testing would be done using CNN.
We configured a deep learning CNN using Caffe (a fast framework for deep learning) and trained it to classify faces into one of the 5 classes based on facial beauty.
The trained model predicts facial beauty with an accuracy of 50.32%.Code Report Presentation
This project is developed during Bitcamp Hackathon.