patrickbrus's Projects
Asteroids Game developed in the Unity Engine in Windows with C# (Open WebGL application)
This repository contains the notebooks for comparing AWS AutoML to manual Scikit-Learn training.
This repo contains code for creating a Birds Classifier and an API where the user can upload a birds image and gets the according species of the bird.
This repository contains the code for creating a small web app where the user can enter an image of a car and gets back a prediction of the trained cars model. The cars model was trained in the Machine Learning Professional Course (can be found in my other repository).
Introduction to Deep Learning course assignments from CS231N
This repository contains the jupyter notebooks and the python code for creating and deploying a sentiment analysis model using Amazons Sagemaker library and the AWS cloud.
This repository contains a react app and some dockerfiles. The goal of this project was to create a CI/CD flow using Docker, Travis CI and AWS.
The Google Cloud Developer's Cheat Sheet
Repository containing the capstone project of the Data Science Specialization from IBM on coursera.
This repo contains all the projects done during the IBM Machine Learning Professional Certificate on Coursera.
Kubernetes Yaml Templates
Repo for jupyter notebooks containing code for small Medium articles.
This repository contains the multi-container application developed during the Docker course taken on Udemy.
This repository contains a multi-container application that is deployed using Kubernetes. The Kubernetes environment is developed locally and in the end productionized using Google Cloud.
This repository contains the jupyter notebooks and the python code for creating a plagiarism detector using Amazons Sagemaker library.
A Machine Learning project for retail data analytics as part of the Machine Learning Engineering Nanodegree Capstone Project from Udacity
✈️ A simple portfolio for developers to showcase their skills and projects. Blog and tutorial at freeCodeCamp.
This repository includes two jupyter notebooks. The first one retrains the already pre-trained ResNet-50 using transfer learning in order to classify fruits from the Kaggle 360 Fruits challenge (https://www.kaggle.com/moltean/fruits). The architecute will be adapted in order to compute the class activation maps within the second notebook.