Mohammad Othman's Projects
This repository provides a Flask web application that harnesses the capabilities of BERT, BART, and RoBERTa models for NLP tasks on the 20 Newsgroups dataset. The application classifies articles, generates concise summaries, and answers user-posed questions.
AmazonReviewNLP is a deep learning project that utilizes LSTMs for sentiment analysis on Amazon customer reviews.
Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.
This project is an end-to-end machine learning pipeline with a focus on efficient model deployment using Flask API, Docker, and Amazon EC2. The modular architecture ensures seamless integration and a consistent experience across environments. A CI/CD pipeline with GitHub Actions streamlines development and deployment.
ChestXGAN is a deep learning project that uses Generative Adversarial Networks (GANs) to generate synthetic chest X-ray images and detect COVID-19 from them. By using GANs, ChestXGAN can produce realistic-looking chest X-ray images that can help in the training and evaluation of machine learning models for detecting COVID-19.
Cheat Sheets
Example Repo for the Udemy Course "Deployment of Machine Learning Models"
This repository exemplifies a robust ML workflow, leveraging MLflow for experiment tracking, Docker for containerization, TensorFlow Serving for model deployment, and GitHub Actions for CI/CD. It embodies a comprehensive system designed to predict diabetes progression using advanced machine learning paradigms.
The Doctor Who project is a database project based on the British science fiction television program. The purpose of the project is to create a database that contains information about the Doctor Who universe, including data on episodes, doctors, companions, and enemies.
DoctorWhoCore is a project that uses Entity Framework Core to manage and manipulate data in a database. This project has a focus on the popular British television show, Doctor Who. The database created by this project contains information about the various characters, enemies, authors, and episodes that have been featured in the show.
This project is a .NET 7 Web API application that serves as a backend for managing Doctor Who related data. It supports CRUD operations for doctors, episodes, and authors, as well as adding companions and enemies to episodes. The application is built using Entity Framework Core for data access, AutoMapper for object mapping, and FluentValidation.
Complete Pipeline for Recommendation System Development and Deployment
A collection of useful .gitignore templates
Arduino-based system that can accept voice to direct commands and process them. The system provides us with the ability to switch any device ON/OFF. The aim of the project “Arduino Based Voice Controlled Home Appliances Using Bluetooth” is to furnish a system that can respond to voice commands and control the status of electrical devices. (2019)
An end-to-end MLOps project integrating Flask, Docker, CI/CD (GitHub Actions), and Kubernetes. This repo demonstrates the development, containerization, automated deployment, and scaling of a simple ML model for iris classification.
This project applies the Longformer model to sentiment analysis using the IMDB movie review dataset. The Longformer model, introduced in "Longformer: The Long-Document Transformer," tackles long document processing with sliding-window and global attention mechanisms. The implementation leverages PyTorch, following the paper's architecture
MathConvNet: Mathematical Convolutional Neural Network Implementation from Scratch
ML AutoTrainer Engine, developed using Streamlit, is an advanced app designed to automate the machine learning workflow. It provides a user-friendly platform for data processing, model training, and prediction, enabling a seamless, code-free interaction for machine learning tasks.
The model is character-based, for each character the model looks up the embedding, runs the GRU one timestep with the embedding as input, and applies the dense layer to generate logits predicting the log-likelihood of the next character.
This is a C# price calculator program that calculates the price of a product with a flat-rate tax, discounts, packaging, transport, and administrative costs. Customers can choose how discounts are combined. The program simulates the evolution of customer requirements over time.
The String Calculator Kata involves building a calculator that can take a string of numbers separated by commas or new lines, and return their sum. The calculator should be able to handle an unknown amount of numbers, and it should also support custom delimiters specified at the beginning of the input string.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent