In this post, I will be talking about a unique way to use reinforcement learning (RL) in deep learning applications. I definitely recommend brushing up some deep learning fundamentals and if possible, some policy gradient fundamentals as well before you get started with this post.
Traditionally, RL is used to solve sequential decision making problems in the video game space or robotics space or any other space where there is a concrete RL task at hand.
Developing RL techniques for IR and NLP applications
Developed siamese deep learning architectures to find similar questions from a Q&A archive on a distance learning platform
Designing a curriculum for job applicants by analysing data on job descriptions. Assignment done for the [MITACS Globalink programme](https://www.mitacs.ca/en/programs/globalink)
Search engine on a nuclear corpus and an outline of an approach to build a factoid-based question answering system