Docker

AI Radiology Copilot (ARC) is a CNN model app to recognize and label brain tumors in MRI scans.

Leonardus is an open source project. It is a minimalist, stack-based programming system designed as a flexible framework for implementing and exploring algorithms. The syntax and semantics of its scripting language, LeoScript, are inspired by PostScript and Forth. It is extended with a prototype-based, object-oriented paradigm, has seamless integration into JupyterLab and Docker, and provides a concise and expressive environment for learning and experimentation.

The project name is a nod to Leonardus Pisanus, who was named and became known as Fibonacci.

Consult the project's GitLab Pages for documentation.

Bachelor Thesis Project - Evaluating WebAssembly as a runtime environment for serverless functions.

Unified project demonstrating both batch analytics and real-time streaming pipelines with Apache Spark:

Batch (PySpark/Jupyter): Processed S&P 500 stock data, applied transformations, and ran distributed computations.

Streaming (Spark + Kafka): Built a streaming pipeline to consume Kafka topics, process messages in real-time, and visualize outputs.

Deployed using Docker and Jupyter for reproducibility.

Exploring the basic functionality of a framework for storing vector datasets in Weaviate

Comment prédire le prix de vente d'une maison en fonction de ses caractéristiques ?

Official inference library for Mistral models

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

The project focuses on developing a predictive tool for chess using recurrent neural network (RNN) models implemented in TensorFlow. Data collection was carried out using web scraping techniques from the website https://www.chess-poster.com.

El proyecto se orienta hacia el desarrollo de una herramienta predictiva para el ajedrez, empleando modelos de red neuronal recurrente (RNN) implementados en TensorFlow. La recopilación de datos se realizó mediante técnicas de web scraping desde la página https://www.chess-poster.com.

This project is designed to analyze text for the presence of sarcasm. It uses machine learning models to classify input text and determine whether it contains sarcasm.

The project utilizes the following technology stack:

FastAPI - for creating the API interface and handling requests Docker - for packaging the application and its dependencies into a container Machine learning models for text sarcasm classification

Project for VTU result analysis, extraction and visualisations.

The complete solution for creating and deploying your own trip planner. https://najdispoj.sk

Simulation of a real hospital scenario with a ML model in production

UniGPT-Assist is a succinct, user-friendly solution that addresses the tangible problem of efficiently navigating through university life, providing a reliable and instant information source for students.

Train YOLOv5 for breed classification on Oxford Pets III dataset from scratch on Google Colab, and serve through Dockerized implementation of a flask-based HTML/JS frontend and an asynchronous API service on FastAPI. Use of MLFlow for logging.

Simple docker code that trains custom GAN network to generate images (bad quality, but quite fast in docker)

Netdata 教學文件

This is a Django web application that uses machine learning to predict whether a product will go on backorder or not. It uses a pre-trained Random Forest Classifier, Decision Tree and LGBM models to make predictions based on various features such as product availability, lead time, and more.

This project I created to show how to develop a docker image to use for data science applications