React

I build Events Web Application using FastAPI, React, and SQLite: Check it out Github Repo: Check it out What Technologies I used Python (Programming Language) FastAPI (Backend Framework) Typescript (Programming Language) React (Frontend Framework) Material UI (Styling) Tailwind CSS (Styling) Vite (Frontend Bundler) SQLite (Database) Docker (Containerization) CloudFlare (DNS and SSL) Github Pages (SPA Hosting) Digital Ocean (Backend Hosting) Github (Version Tracking) Github Action (CI pipeline) How did I build It I implemented the business logic in the FastAPI application. There are a total of four REST endpoints, for four tables making up the application. The database-based used to manage the application data is SQLite. The UI experience was written in Typescript using the react library. The Material UI library was used for icons, and components. The styling was done through the use of tailwind. ...

I created a number writing tool that you can check out here. Technologies Used Github Pages (Hosting & Deployment) React (UI) Python (Prototyping) Pytorch (Model design and training) Typescript (Model prediction) Vite (Building Process) Codebase The entire codebase is available on Github. Design For this web application, I utilized a React frontend and implemented a LeNet model using Pytorch. The model was trained on the MNIST dataset. Project Structure model_exploration: This section covers the training and model exploration processes. notebook.ipynb: A Jupyter notebook containing the model exploration. LeNet5.py: The Pytorch definition of the model. ui: This contains the React application that constitutes the frontend with a standard React structure. Model Training The Pytorch-based LeNet model code can be found here. The model consists of 5 blocks, with 3 being convolution blocks and 2 being feedforward blocks. The activation function used for all blocks except the final layer is Tanh. Training was conducted on the MNIST dataset for 10 epochs, resulting in an impressive test accuracy of 98.6%. ...