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Eric Rubiel's Projects

49 icon 49

Program to test the conjecture: 49x2^n has always and odd digit

awful-ai icon awful-ai

😈Awful AI is a curated list to track current scary usages of AI - hoping to raise awareness

capsule-networks icon capsule-networks

A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".

chinampas icon chinampas

To count polychrony groups, we define Triangular sequences and Chinampas, they are variations on Cellular automata 192.

deep-dream icon deep-dream

My presentation for the FSU machine learning seminar on Deep Dreams

flower icon flower

Flower - A Friendly Federated Learning Framework

funsearch icon funsearch

Simple working implementation for google-deepmind FunSearch algorithm

gpt-researcher icon gpt-researcher

GPT based autonomous agent that does online comprehensive research on any given topic

gradio icon gradio

Create UIs for your machine learning model in Python in 3 minutes

graph-networks icon graph-networks

(Work in progress) My exposition to FSU machine learning seminar on Graph Networks.

hands-on-webscraping icon hands-on-webscraping

This repo is a part of blog series on several web scraping projects where we will explore scraping techniques to crawl data from simple websites to websites using advanced protection.

mat2vec icon mat2vec

Supplementary Materials for Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).

ml-doctorado icon ml-doctorado

El camino mas sencillo para los estudiantes de doctorado que quieran usar ML en su investigacion.

modelbasedmlch3 icon modelbasedmlch3

My presentation for the FSU seminar on Model Based Machine Learning

nlp-text-mining-working-examples icon nlp-text-mining-working-examples

Full working examples for Text Mining and NLP. Gensim word2vec, phrase embeddings, keyword extraction with TF-IDF and sklearn, word count with PySpark

pytorch-lightning icon pytorch-lightning

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

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