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aeros

Project Aeros aims to optimise data science processes in Python, and provides the user with easy to use functions and APIs

ai_lab_2018

ai_lectures_2017

Data Science Society of UNSW Lecture Series

apex

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

bitarray

efficient arrays of booleans for Python

blis

BLAS-like Library Instantiation Software Framework

cria

Finetuning LLaMA with cleaned datasets

cuml

cuML - RAPIDS Machine Learning Library

cupy

NumPy-like API accelerated with CUDA

danielhanchen

danielhanchen.github.io

A collection of machine learning notes

game2019

gemma_pytorch

The official PyTorch implementation of Google's Gemma models

glrm

gov_hack_2017

We used NSW Air Quality data, health data, transport OPAL congestion data and also some novel disease spreading algorithms to try model and find correlations between Air Quality, Congestion and Influenza rates.

health_hack_2017

We developed a data mining solution to scrape data from multiple sources to get the latest and greatest information about health grants. AND - we designed a cool Jupyter Notebook Solution to visualise our 2nd challenge- to get data constantly from a source, and show it in Plotly

hf_blog

llama.cpp

LLM inference in C/C++

llama2.c

Llama 2 Everywhere (L2E)

markovian_sir_deaths_model

I noticed that traditional methods to predict a disease outbreak was by performing sentiment analysis on Twitter posts and Google Search terms. Unfortunately, these methods were inadequate, as Twitter and Google is not popular in all countries. So, I created a system to model and predict outbreaks without the need for social media. The system was able to update the probabilities of a virus from spreading from A to B in real time, and I plan to release it to the public next year. I also used Machine Learning and Deep Learning to predict larger long-term virus trends with Google Trends, and this acted as a validator for the MSIRD model.

mbs_datathon_2017

We created a new Health Rating system that aggregated data from QANTAS, ABS, Victoria Health and supermarket data to reveal correlations between them. We got Special Commendations for our great work!

microsoft_hack_2017

Created a Machine Learning system to predict if someone is going to suicide, and proposed some mechanisms to counter and help potential victims.

moonshot

We're creating a full Earth simulation to predict the future of everything and make JARVIS a reality!

notebooks

RAPIDS Sample Notebooks

notes

peft

🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

pomegranate

Fast, flexible and easy to use probabilistic modelling in Python.

python-markdownify

Convert HTML to Markdown

reversing_markov_chains

Sometimes we want to ”reverse” a Markov Chain process. Taking the inverse of the transition matrix allows this to work, but the inverse result is not a transition matrix. If I wanted to model a population going to work, and then going back home, negative and greater than 1 probabilities in the inverse matrix will cause issues. I propose a method to compute the ”inverse” of a transition matrix, and the result is still a transition matrix

sciblox

sciblox - Easier Data Science and Machine Learning