I am a machine learning engineer who is passionate about using AI to make the world a better place. As a data scientist, I thrive on using statistics to gain insights into the world and make meaningful contributions through the development of AI tools that simplify life.

Connect with me on LinkedIn or reach me at dahshury@gmail.com

A gym back machine detector (7 machines) using RT-DETRv2 and YOLOv8 with images from various online datasets. This can be used in a real-world scenario to identify a gym machine even when humans are using the machine. This is also deployed on Microsoft Azure's Web Apps for containers, front-end using Streamlit.

A marble quality classifier using from scratch implementation of ResNet34, pretrained ResNet50, and Huggingface pretrained ViT transformer.

(Regression task) Predicting the prices of Australian vehicles based on 12 features using Exploratory Data Analysis (EDA), Data cleaning, and feature extraction. This is deployed on GCP App Engine with a Streamlit front-end.

(Classification task) Predicting whether a customer will subscribe to a term deposit based on 12 features.

An application for desktop STT using Insanely-Fast-Whisper and Faster-whisper

WinSTT is an application that leverages the power of OpenAI's Whisper STT model for efficient voice typing functionality. This desktop tool allows users to transcribe speech into text in any application. With support for over 99 languages and the capability to run locally without needing an internet connection.

The existing Windows speech-to-text is slow, inaccurate, and unintuitive. This app provides customizable hotkey activation, and fast and accurate transcription for rapid typing. This is especially useful to those who write articles, blogs, and even in day-to-day conversations.

A UI was also developed into a ".exe" for easier use using PyQT6.