Machine Learning & Data Science Notebooks
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Updated
Sep 20, 2024 - Jupyter Notebook
Machine Learning & Data Science Notebooks
Course project designed as TA for Linear Algebra, utilizing Markov Chains to model economic dynamics.
CellRank: dynamics from multi-view single-cell data
A simple melody generator based on Markov chains
A Markov Chain Text Generator combining Go's efficiency with Python and Streamlit. Generate new text from learned patterns using a hybrid Go-Python implementation and user-friendly web interface. making it ideal for text generation enthusiasts and those interested in Go-Python integrations.
Fast, accurate taxonomic assignments for the human vaginal microbiota
Exploratory spatiotemporal data analysis and Geospatial distribution dynamics analysis
Markify is an open source command line application written in python which scrapes data from your social media accounts and utilises markov chains to generate new sentences based on the scraped data
⛓️ Python package which provides you a simple way to generate phrases using Markov chains.
A suite of codes for performing discrete time Markov chain random walks with neuromorphic hardware and simulators.
MCMT book re-written in jupyter notebooks with relevant code to simulate the concepts.
Auto generate tweets powered by pre-trained GPT2 based large language model (LLM) available offline.
This repository contains the Linear Algebra computational labs I completed throughout the semester, which provided practical applications of the content intersection between linear algebra and computing
This is a collection of scripts and functions that serve as exercises for understanding and writing various numerical methods from scratch.
LexRank algorithm for text summarization
Generative art system using Markov chains and quinary logic
Generate various kinds of names using Markov Chains and random mixers
Simple generative writing with Markov chains
A software package for algebraic, geometric and combinatorial problems on linear spaces. By R. Hemmecke, R. Hemmecke, M. Köppe, P. Malkin, M. Walter
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