Name: Quan Hoang Ngoc
Type: User
Company: UIT
Bio: I am a student at a university.
I am learning about Mindset, Computer Science, Data Science and Software Engineering (CS).
So, I enjoy life.
Location: Ho Chi Minh City, Viet Nam
Quan Hoang Ngoc's Projects
The Qualcomm® AI Hub Models are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.
Quan and Giang
In this project, we install search algorithms such as DFS, BFS, UCS to apply them to solve the sokoban game by modeling them as a search problem.
We design a new heuristic function for the A* algorithm and make a benchmark to evaluate its performance with the default heuristic function as well as the UCS algorithm
Resources for Multiple Object Tracking (MOT)
This is the Final Project about CNNs and Backpropagation for CS115.tkhmt
This is an interaction app that helps manage Coffee Shop on local devices.
Môn học Phương pháp nghiên cưu khoa học
This is the web-app that help virtual try on shoes for customers. It is MVP that we take part in Top 24 Datathon.
This is the app that help classify gender base on face image, use Keras library training
This is the project where we develop a library about image operations.
One-step image-to-image with Stable Diffusion turbo: sketch2image, day2night, and more
This computer vision project utilizes YOLOv7 and DeepSORT Algorithm for multi-object tracking.
🐚 OpenDevin: Code Less, Make More
In this project, we conducted some experiments to measure and evaluate the solution and performance of OR-Tools for Knapsack problems. Thus, we also learn how to use OR-Tools and install it for practical purposes.
This is some useful Exam Experiences and Books about CP that I practiced ago.
Multi-Class Multi-Movement Vehicle Counting
In this project, urban traffic videos are collected from the middle section of Xi 'an South Second Ring Road with a large traffic flow, and interval frames are extracted from the videos to produce data sets for training and verification of YOLO V5 neural network. Combined with the detection results, the open-source vehicle depth model data set is u