stephen-osullivan Goto Github PK

auto-random-forest-app

An app in streamlit that allows the user to upload data and fits and random forest to it. Deplots to google cloud run using Docker.

autoencoder

AutoEncoder for tabular data. Works on pytorch or in sklean pipelines.

automl-app

An app coming pycaret automl functionalities and streamlit

aws-mlflow

simple repo to test connecting to a remove mlflow server on aws

azure-ml-nb15

Using Azure ML to build and ML pipeline on the UNSW-NB15 cybersecurity dataset.

chainlit

experimenting with chainlit and chatgpt

cs-first-app

checking distribution of simple c share console app

cs50web

notes and work for cs50w (web design course)

ctransformers-llama3

d2lai

Working through the DL2.AI book: https://d2l.ai

database-client-app

Simple app to interact with a local database.

deep-face

Experimenting with the deepface computer vision library

deep-learning-examples

A repo to show some examples of deep learning in action.

docker-taxi-data-pipeline

using docker compose, postgress and other tools to create a data engineering pipeline.

ds-and-algs

data structures and algorithms in python

ds-templates

Some template notebooks and code for data science use cases.

fastai

Examples using the fastai library

flask-docker-app

simple example of using flask with docker

gcp-computer-vision

repo to explore computer vision on gcp

github-actions-practice

Simple project to test github actions for automated testing + integration

hugging-face

hugging face basics

hugo-cd-aws

Example of Continuous Delivery Pipeline for deploying a HUGO website to AWS.

langchain_01

examples of langchain 0.1 solutions

linear-regression-gradient-descent

llamacpp

experimenting with llamacpp

llm-code-dlp-scanner

Project to investigate using LLMs to scan code for data leaks.

llm-sql-writer

Use LLMs to write SQL code.

llm-vecdb

Experimenting with using chromadb vector databased to augment an LLM.

logistic-regression

Logistic regression from first principles. Gradient descent used to fit.

ml-deploy-app-example

Here we do some EDA and fit some models to the famous breast cancer dataset. Once this is done we convert our model into an app using flask.