This repository contains the code and analysis for a research project aimed at enhancing social media impact for food safety security organizations. The project focuses on understanding text tone preferences based on user demographics using machine learning techniques.

螺旋熵减系统

IU Projects

Automatic Gap-Fill Question Generation

This repository gives you access to the CLIMATEREADY survey dataset containing thermal comfort votes during the 2021 and 2022 heatwave periods in Pamplona, Spain, as well as other relevant parameters self-reported by surveyees (e.g. occupant characteristics and behaviour, key building/dwelling characteristics, sleep problems, heat-related symptoms)

ML/DL project for unsupervised traffic segmentation. We used a YOLOv5 model as our encoder and created a custom UNet for our decoder

efficient graph partitioning

Nonnegative Matrix Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning

repo contains sample code snippets on #ML, #DeepLearning, #GenAI topics

螺旋熵减理论

Machine Learning Clustering Techniques for Customer Segmentation - Certification Project

Code supplement for "Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction"

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

for various machine learning projects

Task-2 Completed as a DSBA Intern @ The Sparks Foundation

In this section, we will examine unsupervised learning algorithms in machine learning.

Multimodal Unsupervised Machine Translation and Unsupervised Representation Learning for high-level model interpretability.

Stylometry approach detecting writing patterns and changings using NLTK, XML-roBERTa, Gensim topic modelling and unsupervised-PCA learning

The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to evolve and train Artificial Neural Networks without error back propagation

Free lectures on Machine learning which covers core topics and models on supervised and unsupervised, machine learning.