Python

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%. ...

I build maze solving game using Pygame: Check it out What Technologies I used Python (Programming Language) Pytest (Unit testing) Pygame (Visualization & Keyboard Events) Pygbag (Python to wasm tool for Web Distribution) Github Pages (Hosting) Github Action (CI pipeline) Github (Version Tracking) How did I build It Codebase Github Repo Design The game is built using the MVC (Model-View-Controller) architecture and is based on Wesley Werner’s repository. I highly recommend checking out his repository for better understanding. ...

Viola-Jones Algorithm Implement the Viola-Jones Algorithm for rapid face detection in python from scratch. First developed a feature extraction script, which extracted 2.5 thousand features from a 19 by 19 grayscale image. I applied the feature extraction script to a 2000 image of non-faces and 500 images of faces. I implemented the AdaBoost algorithm through the python multiprocessor module, leading to a decrease in execution time by 20%. I ran 10 rounds of the algorithm to achieve an empirical error of 67% on the testing data set. Feature manipulated the cost function on the algorithm to priories false-positive, which led to a 5.4% false-positive error. ...

PPG signal Extraction Used Matlab to extract the RPG matrix for a single frame from a 720p video file with a duration of 10 to 15 seconds. The RPG matrix was converted into gray-scale single value matrix to simplify the calculation. The dimension of the gray-scale matrix was changed to focus on the area of interest to increase the computation time of the program. The gray-scale signal was extracted from the video and was studies in the frequency domain. An IIR filter was applied to the single to extract the PPG signal. ...