Mavengence's Stars
seanb-github/seanb-github
Config files for my GitHub profile.
leits/MeetingBar
🇺🇦 Your meetings at your fingertips in the macOS menu bar
KindXiaoming/pykan
Kolmogorov Arnold Networks
awslabs/gluonts
Probabilistic time series modeling in Python
SchwinnL/LLM_Embedding_Attack
Code to conduct an embedding attack on LLMs
Nixtla/neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
EQ-bench/EQ-Bench
A benchmark for emotional intelligence in large language models
koaning/arxiv-frontpage
My personal frontpage app
mad-lab-fau/CapMIT1003
Dataset of human visual attention during captioning
RobbenRibery/TuoTuo
TuoTuo is a Topic Modeling library for Researchers and Engineers
lamini-ai/lamini
The Official Python Client for Lamini's API
mad-lab-fau/trend-detection-in-healthcare-podcast-data-set
Artificial Intelligence Trend Analysis on Healthcare Podcasts using Topic Modelling and Sentiment Analysis - A Data-Driven Approach
google-research/tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
toUpperCase78/formula1-datasets
Datasets & Analyses for Formula 1 World Championship
mad-lab-fau/trend-detection-data-set_politics
Artificial Intelligence Trend Analysis in German Business and Politics - A Web Mining Approach
FrederikBoehm/Bachelorarbeit_Text
TeX files and compiled PDF of my bachelor thesis.
FrederikBoehm/Renderer2
Physically based path tracer for volume and mesh rendering used in my master thesis.
FrederikBoehm/master_thesis
LaTeX code for my master thesis
bohniti/papyri_helper
This repository contains the source code for my master thesis.
huggingface/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
MIND-Lab/OCTIS
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
ordass00/IT-Projekt
University IT-Project: Web application for creating a nutrition plan based on user parameters.
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.
bohniti/climate-challenge
sourcerer-io/sourcerer-app
🦄 Sourcerer app makes a visual profile from your GitHub and git repositories.
bohniti/Seattle-Airbnb-Open-Data
predict seattle Airbnb rental prices
bohniti/Computing-Transparent-Decisions
Final year project as undergraduate student of business information systems and management
bohniti/Generating-Realistic-Images
Generation of Real Looking Images Using GANs
bohniti/The-CIFAR-10-dataset
Identify the subject of 60,000 labeled images
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.