Python

A competition organised by Terence Tao and Damek Davis asked whether mathematical reasoning can be compressed into a page a language model reads before answering. Then its second stage removed the thing that made the first stage easy to fake: an answer was no longer worth anything unless it arrived with a proof a machine would check. This is what I built for both stages, and the moment that convinced me a confident answer and a correct one are completely different products.

There is a question in mathematics that has been open since the 1800s, and for one slice of it you can now attack it by search. I built a factory that produced degree-24 polynomials, submitted ten thousand scoreable pairs to a competition organised with the LMFDB and Terence Tao, and finished 54th of 256 with a score of 2.36. The interesting part is not the rank. It is that I can prove, with numbers, exactly why the score was small, and the answer says something about how these searches should be run.

Special thanks to Sarvesh Talele for his meaningful contributions, support, and wisdom that helped shape this work. Traffic cameras record the circumstances of a great many collisions, but methods that read those recordings automatically are usually trained on annotated footage from the same camera. This work asks what can be recovered without any labelled real-world data at all, using three independent modules and only pre-trained weights.

Regression analysis in supervised machine learning, from Simple Linear Regression to Multiple Linear Regression, with the mathematics stated plainly and a worked implementation predicting grades from study hours. Covers the assumptions the model rests on, how the coefficients are fitted and read, and how to tell a fit that generalises from one that only looks good.

Special thanks to Mega Satish for her meaningful contributions, support, and wisdom that helped shape this work. Clock discrepancies are troublesome in distributed systems and pose major difficulties. To avoid mistakes, the clocks of separate CPUs must be synced. This is to ensure that communication and resource sharing are as efficient as possible.

Special thanks to Mega Satish for her meaningful contributions, support, and wisdom that helped shape this work. The core aim of this project is to take raw data, analyse it, and perform exploratory data analysis to clearly understand the underlying patterns. Using these insights, we build and train a Neural Network model to achieve accurate results. Finally, the complete system is deployed as a web application.

Special thanks to Mega Satish for her meaningful contributions, support, and wisdom that helped shape this work. We propose to implement a house price prediction model of Bangalore, India. It’s a Machine Learning model which integrates Data Science and Web Development. We have deployed the app on the Heroku Cloud Application Platform. Housing prices fluctuate on a daily basis and are sometimes exaggerated rather than based on worth.

Special thanks to Mega Satish and Hasan Rizvi for their meaningful contributions, support, and wisdom that helped shape this work. In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos.