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Name: Sam Purkis

Type: User

Location: Totton

Hello there, my name is Sam 👋

I'm a Christian, Developer, ML/AI Lover and lifetime Learner!

I'm constantly learning and tinkering with Artificial Intelligence 🤖 and Machine Learning, example.
I am currently learning PyTorch 🔥, as I've found TensorFlow a bit buggy 🐛.
I am learning more Javascript, to show more of what AI can do, simple example.
I'm also learning Rust, mainly as I'm interested in WebAssembly (Wasm).
I love Python (the best language 🐍), and enjoy seeing what I can do with it and increasing its performance.
I would like to contribute to more open source projects 😄.

Project Highlights

Python

Face Detection from Scratch - Face detection using Yolo architecture from scratch. Uses Pytorch/Pytorch Lightning as the ML framework
Chess CNN - Convolutional Neural Network trained using supervised learning on grandmaster chess players (sourced from Lichess). Uses Keras.
Animal Population Simulation - Population simulation of "foxes", "pigs" and "grass" to see how the patterns change with initial conditions and hyperparameters. Uses Numpy and OOP practices.
Face Detection App - Fun little project, overlays swag glasses on the face. Built using Kivy (cross platform, mobile and desktop) and OpenCV.
Fast Json Normalize - A fast implementation of json_normalize function in Pandas (has been merged, see here).
Auto Beatsage - Gluing together YouTube to Beatsage with my Oculus Quest to add any YouTube song as a Beatsaber song.
Autocompile - Declarator to compile and speed up a function with Cython harnessing the type hints for type information. Uses Cython.
Crypto Price Dashboard - (WIP) Dashboard showing the price of different cryptocurrencies over time. Uses APIs and Dash.
Doorbell project - (WIP) A smart doorbell that announces who is at the door as they approach (e.g. Amazon delivery, Mother in Law)
PyTorch Playground - Collection of Colab Notebooks of me exploring different aspects of ML and PyTorch
Data Science with Python - A readme I made for a friend giving description of different Python libraries used for Data Science

JavaScript

Motion controlled Snake - Motion controlled snake game uses webcam. Build using React and TensorFlow JS.
Dota Heroes Website - Information website of Dota Heroes, using multiple APIs. Build using Svelte.

My links

Languages and Tools

These are languages, tools and projects I have experience with, either from work or on personal projects (not exhaustive).

Python Python

Javascript

Rust Rust

Wasm Bindgen

Sam Purkis's Projects

ai_sermon_whisper

Based on https://github.com/openai/whisper, the purpose of this repo is to make notes from sermon audio in any way possible

array_benchmarks

Comparing as many languages as possible, using same array algorithm

auto_beatsage

A gui/command-line tool to download and deploy Youtube and Soundcloud songs and playlists directly through beatsage and install them onto your BeatSaber

autocompile

Speed up Python with type hints (by converting it inline with Cython)

bash_command_finder

Find bash commands by describing them in plain english

cat-cam

chat-ui

Open source codebase powering the HuggingChat app

chess_cnn_supervised

Train a chess CNN on collection of Grandmaster games

chessxtra-game

core

:house_with_garden: Open source home automation that puts local control and privacy first.

crypto_discord_bot

Playing around with discord bot for helpful notifications

cython_debugger

Work in progress, to hook up cygdb to a server client framework for Cython debugging (like VS code)

dash_crypto_price_dashboard

Dashboard to track Crypto prices using Dash and CoinGecko API

data-science-with-python

A list of the main Python packages I've used over the past few years, made for a friend looking to go into ML Research.

dino_game

Simple Dino game

doorbell_project

In short: reads video from a camera (e.g. at front door), defines who is in it against a set of known people and says who is in the video using text-to-speech

fast_json_normalize

A package designed to be a fast implementation for the pandas json normalize function, as it can be rather slow when dealing with very large json objects