Docker

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

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.

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.

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

This repo contains a python parser that processes the reports of several tools used to find XSS vulnerabilities. In specific this analyzer work with CaptureDOM, a capture the flag environment realized properly for this aim. The analyzer submits any level of CaptureDOM to each tool.

The knack lies in learning how to throw yourself at the ground and miss.

Custom toolbox for geospatial development

Wraps JupyterLab within a Docker image. It adds support for other languages (Java) in addition to the standard Python kernel. The Docker image can be used locally or with cloud offerings.

docker nuxt flask