Undergraduate Works

A collection of projects and coursework from my time in university.

PROJECTS

TinyMLOps Pipeline

For my capstone project, I developed a TinyMLOps Pipeline with the purpose of making tinyML application development and deployment easier.

  • Languages used: C, C++, and Python3.
  • Tools: CMake, Make, and Docker.
  • Deep Learning Framework: Tensorflow Lite Micro.
  • MCU Hardware: STM32F746G.
View on GitHub

COURSEWORK

Advanced Software Concepts

Tackled in-depth knowledge and practical experience in developing industry-grade software applications.

  • Developed object-oriented applications and RESTful web services
  • Applied software design principles and diverse software architectures
  • Used tools such as Trello, Git, Maven, IntelliJ, Spring Boot, Docker, and Swagger

Deep Learning

Learned essential Deep Leaning concepts including MLPs, CNNs, RNNs, and Transformers. The course combined theory with hands-on coding projects.

  • Built a Word Sense Disambiguation webapp on Hugging Face using Python3, PyTorch, and Gradio.
  • Trained and evaluated a CNN model for Object Detection using Python3, PyTorch, and OpenCV.
  • Deveoped a transformer based Keyword Spotting model using Python3 and PyTorch.
View on HuggingFace View on Github

Introduction to Information and Complexity

Took an advanced course that explored core concepts like Shannon's Information Theory, Turing Machines, and Kolmogorov Complexity. Built Python3 scripts for the following:

  • Entropy, Source Coding, Channel Coding, Polar Codes
  • Time/Space Complexity, Computability, and Algorithmic Entropy
  • Advanced topics: Data-Processing Inequalities, AEP, and Rate-Distortion Theory
View on Github