- Programming Language: Python, C/C++, C#.
- Framework: Tensorflow, Pytorch, CUDA.
- ML Engineering: Google Cloud, Docker, ML Flow, Digital Ocean.
- Software Engineer: Flask, Streamlit, HTML/CSS.
- Tools & Platforms: Git/Github, SSH.
Technical Skill
Project
Quantization Neural Machine Translation [Link]
- Developed English-Vietnamese translation model using Transformer architecture from "Attention is All You Need.".
- Trained on the PhoMT dataset with 3 million sentence pairs, achieving a BLEU score of 0.26 on the test set.
- Applied asymmetric post-training quantization to multi-head attention layers, converting float32 parameters to int8, resulting in a 20MB reduction in model size.
GPU Computation [Link]
- Accelerated Convolutional Neural Network inference (2D convolution, pooling, fully connected layers) using CUDA C++ for fast prediction.
- Implemented parallel matrix operations, including LU decomposition and image processing (edge detection, sharpening, and blurring) on GPU.
- Applied Seam Carving for intelligent resizing and Histogram Equalization for image enhancement.
Emoji Search Engine [Link]
- Developed a search engine that allows users to find emojis based on input text.
- Improved data quality by processing and augmenting an emoji dataset. Utilized GloVe embeddings with XGBoost for emotion prediction.
- Deployed web application on DigitalOcean using Flask backend.
Hockey Match Analysis [Link]
- Developed a real-time hockey match analysis web app with Flask, Streamlit, and Docker for deployment.
- Used NHL APIs to retrieve and analyze hockey data with statistical, cleaning data, and visualization techniques.
- Trained an XGBoost model to predict shot goal probability and used Comet for data version control.
Generative Adversarial Network [Link]
- Applied image generative techniques, including GAN, Semi-GAN, Conditional GAN, Progressive Growing GAN, and Cycle GAN, to generate high-quality synthetic images.
- Key technologies: Python, Tensorflow/Keras.