Nik Schaefer's Projects
lambda-go-api-proxy makes it easy to port APIs written with Go frameworks such as Gin (https://gin-gonic.github.io/gin/ ) to AWS Lambda and Amazon API Gateway.
This is a Website built to Quickly design a Box Shadow and Copy The CSS
CIFAR10 dataset Image Classification Machine Learning
Text generation based on the Dungeons & Dragons series Critical Role
Autoencoder to denoise images based on the fashion_mnist dataset
An online collection of developer related discord servers — because programmers need more places to argue about tabs vs. spaces
Django Backend Rest Api with a Postgres DB in a Docker Container
MIT Django Backend React Typescript Frontend Template configured for Easy Deployment with heroku
TensorFlow documentation
ML model to distinguish images as dogs or cats
⚡️ Express inspired web framework written in Go
Python Script run on Chromium to add all discord friends with a set name
Build blazing fast, modern apps and websites with React
🚀 Production-Ready Golang Rest API built with GORM, Fiber, and PostgreSQL. Running in a docker container with Hot Reload.
A dog lover's Instagram clone powered by an API, showcasing adorable images — because who can resist cute dogs?
Machine Learning Model to predict handwritten digits based on the mnist dataset of handwritten digits
My Personal Golang CLI for productivity and networking
Golang functions to connect and disconnect from networks
2 layer neural network using only numpy (no tf/keras)
Implementation of Neural Style Transfer using a GAN
☂️ NextJS Boilerplate with TailwindCSS, Typescript, shadcn/ui, and Mixpanel. For CI/CD with Vercel
👋 Hello, My Profile README
My Portfolio Site | Made with NextJS & TailwindCSS
Better look at a Github Users Profile and Stats
My Developer Portfolio, Check it out!
Personal Portfolio V2
Taken data from A list Of Public Api in an md format taken into a json Object
Creating captivating quotes from a single word input in PyTorch
Shakespearean Text Generation with a RNN. (TF/Keras)
Review prediction and analysis using a bi-directional RNN