Successfully developed a fine-tuned BERT transformer model which can accurately classify symptoms to their corresponding diseases upto an accuracy of 89%.

Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.

Successfully established a text summarization model using Seq2Seq modeling with Luong Attention, which can give a short and concise summary of the global news headlines.

Successfully established a Seq2Seq with attention model which can perform English to Spanish language translation up to an accuracy of almost 97%.

This Git repository contains the code and documentation for my progress on developing models for the enhancing road safety project. The goal of the project is to use machine learning algorithms to improve the accuracy of detecting traffic signs and reduce the risk of accidents on the roads.

The motivation for this project is for people should be able to create a model using Keras Library, train it, and then export it to the ModECI MDF environment in order to use it with any MDF compatible tools.

Final Year Project - Optimize and Deploy Machine Learning on Embedded Devices for Manufacturing Application - Pending Submission of Journals to Cluster Computing