Pinned Repositories
trend-detection-data-set_politics
Artificial Intelligence Trend Analysis in German Business and Politics - A Web Mining Approach
Bachelor-Thesis-Textgeneration-TH.OHM
IT-Based textgeneration with the use of NLP methods. A text summarization task is conducted with the amazon fine food review dataset from Kaggle. This task is done by attention and lstm neural networks.
CIFAR-10-CS4487-Assignment
Final project of my Course Machine Learning CS4487 as exchange student at the City University of Hongkong.
COVID19-Germany-Region-Case-Prediction
This was my final assignment for the course Clinical Data Science at the Friedrich Alexander University Department of Health for Master of Computer Science.
GapStatistics
Computing the gap statistics from Tibshirani et. al. for various clustering algorithms
Kaggle-Seattle-Airbnb-Analysis-IS4861-Assignment
Final project of my Course Machine Learning for Business IS4681 as exchange student at the City University of Hongkong.
linkedin-job-scraper-data-analysis
Jobs have been scraped from linkedin to perform a data analysis to optimize the CV and cover letter
Machine_Learning_in_the_Industry_4.0_Seminar_SS2020.FAU
Identifying a Trial Population for Clinical Studies on Diabetes Drug Testing with Neural Networks
Pattern_Analysis_SS20_FAU
Representation-Learning-for-Gait-Analysis-in-Parkinson-s-Patients
This project aims to quantify how accurately Morbus Parkinson's can be classified by different types of deep learning architecture without preprocessing the original sensor data. For this purpose, four different architectures (LSTM, ResNet, a basic autoencoder and a ResNet autoencoder) were used to evaluate the accuracy. The data was collected from patients at the University Hospital of Erlangen. Different severity levels of Parkinson's were regarded as being deceased. In this regard, this project performed a binary classification task (healthy and deceased). It shows, that a ResNet autoencoder predicts Parkinson with 87% accuracy and can be used as a decision support system for doctors.
Mavengence's Repositories
Mavengence/Pattern_Analysis_SS20_FAU
Mavengence/Representation-Learning-for-Gait-Analysis-in-Parkinson-s-Patients
This project aims to quantify how accurately Morbus Parkinson's can be classified by different types of deep learning architecture without preprocessing the original sensor data. For this purpose, four different architectures (LSTM, ResNet, a basic autoencoder and a ResNet autoencoder) were used to evaluate the accuracy. The data was collected from patients at the University Hospital of Erlangen. Different severity levels of Parkinson's were regarded as being deceased. In this regard, this project performed a binary classification task (healthy and deceased). It shows, that a ResNet autoencoder predicts Parkinson with 87% accuracy and can be used as a decision support system for doctors.
Mavengence/GapStatistics
Computing the gap statistics from Tibshirani et. al. for various clustering algorithms
Mavengence/Kaggle-Seattle-Airbnb-Analysis-IS4861-Assignment
Final project of my Course Machine Learning for Business IS4681 as exchange student at the City University of Hongkong.
Mavengence/linkedin-job-scraper-data-analysis
Jobs have been scraped from linkedin to perform a data analysis to optimize the CV and cover letter
Mavengence/Bachelor-Thesis-Textgeneration-TH.OHM
IT-Based textgeneration with the use of NLP methods. A text summarization task is conducted with the amazon fine food review dataset from Kaggle. This task is done by attention and lstm neural networks.
Mavengence/CIFAR-10-CS4487-Assignment
Final project of my Course Machine Learning CS4487 as exchange student at the City University of Hongkong.
Mavengence/Clustering-KMeans-Python-IS4861-Assignment
Clustering Assignment of my Course Machine Learning for Business IS4681 as exchange student at the City University of Hongkong.
Mavengence/DQL_Connect4_Mathematical_Data_Science_SS2020.FAU
Final Project of the Master course Mathematical Data Science at the Friedrich Alexander University Erlangen-Nürnberg. We received the grade A for this.
Mavengence/IT-Project-Autonomous-RCCar-TH.OHM
University IT-Project: Autonomously driving Remote Control Car
Mavengence/Programming-Microcontrollers-Elective-TH.OHM
IT project for the elective course Programming Microcontrollers as undergrad student at the University of Applied Science Georg Simon OHM in Nuremberg, Germany
Mavengence/COVID19-Germany-Region-Case-Prediction
This was my final assignment for the course Clinical Data Science at the Friedrich Alexander University Department of Health for Master of Computer Science.
Mavengence/Machine_Learning_in_the_Industry_4.0_Seminar_SS2020.FAU
Identifying a Trial Population for Clinical Studies on Diabetes Drug Testing with Neural Networks
Mavengence/mavengence.github.io
Mavengence/arxiv-frontpage
My personal frontpage app
Mavengence/Climate_Challenge_Hackathon
Mavengence/data-science-interviews
Data science interview questions and answers
Mavengence/Disaster-Response-Unit-Figure_Eight
A Machine Learning pipeline to categorize disaster events so that you can send the messages to an appropriate disaster relief agency
Mavengence/Mavengence
Mavengence/scikit-learn
scikit-learn: machine learning in Python
Mavengence/Style-Classification-in-Posters
Mavengence/trend-detection-data-set_politics
Artificial Intelligence Trend Analysis in German Business and Politics - A Web Mining Approach
Mavengence/tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.