I dive into various NLP techniques, and focus on Natural Language Generation. I have experience using transfer learning for fine-tuning task specific deep neural nets.
ML is way more than being able to use packages. I code the algorithms to understand its nuances and how the packages get to an efficient implementation. I find that the math becomes easier along the way of implementing it.
Every now and then I find myself spending hours improving efficiency of my code and its always a fun exercise. The end result of seeing the execution time reduce by a very high rate brings a feeling of satisfaction.
I try to incorporate, for dev and prod environments, containerization tools like Docker, conda, and venv just so that I don't have to scream "But it worked on my laptop!" in the end.
In the absence of a discourse marker, splitting a sentence at the point of discourse is tricky and such discourse based splitting is quite useful in many NLP tasks. I fine tuned ELECTRA with an appropriate head using transformers library to achieve 91.8% test accuracy in 2 epochs.
Used SQuAD 1.1 to train a seq2seq model that employs copy mechanism to generate questions given a pair of context and answer. All code for the model architecture was written using TensorFlow 2.2. Published copynet-tf which can be trained for any seq2seq task that would benefit from copy mechanism. Questions generated could predict answers with 18% lesser F1 score compared to original questions.
An interdisciplinary research work where ML was used to automate the referral decision of endodontic cases, which was deployed as a mobile app to use at a busy Nair Dental Hospital, Mumbai. Published in Clinical Oral Investigations, Aug. 2019
Explored the generalizability of an attention-driven GAN model by trying latent space interpolations and understanding the role of the latent vector. The model was also tightly dependent on a particular sentence syntax. It was for an individual project work of CS 7180
Employed various ensemble model techniques to make a tri-class predictor of traffic density in a given location at a given time using over a half a year of crawled data. It was my team's final year undergrad project which was published in ICSCET, an IEEE conference and IJRASET in 2018
A playground repository for various ML algorithms which I implemented as I was learning about them. They may not be optimized like sklearn but it laid a very strong base about my understanding of how things work under the hood.
An Android app for managing bunks from tracking attendance to even planning bunks
A shell script that converts a csv file (Excel Sheet) to an SQL file that can be imported in a MySQL database
A python script to hide information over an audio file in .wav format
RPL network simulation using 6lowpan for a hospital, with security services added
Web Technologies mini project, Virtual Stock Market
A Packet Sniffer app made using Python for Linux
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