TK's repositories
graphql_prac
GraphQL Full-stack application made w/ GraphQL, Apollo-server, Urql, PostgreSQL, TypeORM, Redis, Typescript, NextJS, DataLoader
apollo-client
:rocket: A fully-featured, production ready caching GraphQL client for every UI framework and GraphQL server.
astro-docs
Astro documentation
autocomplete
IDE-style autocomplete for your existing terminal & shell
Digit_Prediction_GPU
Made a model to predict numerical digits with the MNIST dataset using pytorch and torchvision. The model was trained on a GPU and perfomed very well: 97% accuracy.
distributed_db_consistency_models
Implementation of Different Consistency Models in a Distributed Database
homebrew-tap
Homebrew tap for my apps
Java_kafka
Implementation of a kafka producer + consumer using kafka-clients java library
sections_graphql_server
GraphQL server using PostgresQL + Redis. Made w/ Apollo server and type-graphql for sections client.
GoLangHTTP
Making a REST API w/ Golang using gin.
Image_Classification_ResNet9
Image classification model using cifar10 dataset. Model was trained using ResNet9 structure with pytorch and was trained on the GPU.
LR_Ecommerce
Data exploration and using skit-learn to perform machine learning algorithms to help make decisions based of results gathered from the data,
MERN-Ecommerce-Store
E-commerce store built with React, Node.js, Express.js, MongoDB, and Redux.
NLP-Email-Spam-Detector
Used a dataset that already had some emails that were classified as spam or not spam. Then clean up the data and removed punctuation and common stopword. After than, I used some sckit-learn's feature extraction class to apply Count Vectorizer and then Tfidftransform. I then applied sklearn's MultinomialNB classifier on that data. I used that trained model to predict if an email was spam or not on a test data and used sklearn's metrics to see how well my model performed.
oh-my-posh
A prompt theme engine for any shell.
Rust_WordCount
A command line application that inputs a .txt file format and returns the occurrence of the maximum number of words in that file. Integrated std::fs,std::env and std::io modules in order to implement this program.
Sentiment-Analysis
The model predicts weather a tweet has a positive meaning(1) or a negative meaning(0).
signed_backend
Backend for https://github.com/tk04/signed. Made with Typescript, Nodejs,Redis, ExpressJS, MongoDB, and Socket.io. Hosted on Heroku @ https://signed-be.herokuapp.com/
wcLinux_impl
wc linux command implementation in C