This is a collection of my personal projects that I work on in my free time. Hope you like them.


An end-to-end Deep Learning platform that allows users to create, train, and deploy their own neural network models in a matter of minutes without writing a single line of code.

The platform supports two types of tasks:

  1. Image Classification
    Classify images from your own dataset by using them to train a ResNet-34 or MobileNet v2 model. Training happens via transfer learning where models available will be pre-trained on the ImageNet dataset.

  2. Sentiment Analysis
    Predict sentiment from sentences by training a LSTM or GRU based sequential model on your own dataset. The models will be trained from scratch.

Tools Used: Python, PyTorch, JavaScript, ReactJS, Redux, AWS (Lambda, EC2, S3)


A high-level deep learning library built on top of PyTorch to solve computer vision problems.

Tools Used: Python, PyTorch

Topic Based Image Captioning

An automatic image caption generation system built using Deep Learning.

  • Devised a novel image captioning model using CNNs and LSTMs trained on MSCOCO-2017 Dataset.
  • Created a system where LSTMs were given additional information (topics) extracted from image captions using Latent Dirichlet Allocation (LDA).

Tools Used: Python, Tensoflow, NLTK, OpenCV

Stock Bridge

An online stock market simulator enabling users to have an experience of trading in the real-world market.

Tools Used: Python, Django, Django REST Framework, Bootstrap, Heroku, sendgrid

Code Warrior

An online judge platform with support for languages C, C++, and Python.

Tools Used: Python, Django, Bootstrap, Amazon Web Services, PythonAnywhere, sendgrid.


An E-commerce website built using Django.

  • Built the backend on entirely on Django. Utilized jQuery to make the website asynchronous.
  • Developed features like checkout with online payment, send order receipt via email, selling digital items e.t.c.

Tools Used: Python, Django, Bootstrap, jQuery, Ajax, jsrender, stripe, mailchimp, Amazon Web Services, heroku, sendgrid

Autoranking Amazon Reviews

Ranking the reviews on Amazon according to their helpfulness score.

  • The problem was modeled as a regression problem. The performance was evaluated by using the coefficient of determination and rank correlation.
  • Predictions were made based on various categories of features of the review text, and other metadata associated with the review, with the purpose of generating a rank for a given list of reviews.

Tools Used: Python, Numpy, Pandas, textblob, scikit-learn