Machine Learning

A calculator multiplies two thousand-digit numbers and takes the remainder without thinking about it. Ask a neural network to do the same thing and it becomes an open research question, one that Terence Tao helped set as a competition. The answer has to be exact, because a remainder that is off by one is simply wrong. This is what I submitted to the SAIR Foundation’s Modular Arithmetic Challenge, why the obvious approach cannot work, and the one idea that made the difference.

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. The purpose of this paper is to introduce the Julia programming language with a concentration on Text Summarization. An extractive summarization algorithm is used for summarizing. Julia’s evolution and features, as well as comparisons to other programming languages, are briefly discussed.

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 Archit Konde for his meaningful contributions, support, and wisdom that helped shape this work. The purpose of this study is to familiarise the reader with the foundations of neural networks. Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs).