Thang Nguyen-Duc's Projects
Active learning for deep object detection using YOLO
📚 Papers & articles of companies sharing their work on applied data science & machine learning.
:metal: awesome-semantic-segmentation
Learn about BERT and variations. Apply to the problem of supporting phrase extraction for emotional analysis.
BFS Parallel algorithm with openMP using Bag structure
Project code for cd0581 refresh taught by Giacomo Vianello
Phương pháp trọng số giải bài toán biên Dirichlet (Môn: Phương pháp phần tử hữu hạn)
Khóa học C++ và thuật toán cơ bản
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Apply deep learning in solving specific derivative equations
A paper list of object detection using deep learning.
It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.
Detect field with Mask RCNN, Instances segementation, Object Detect
This repository contains few-shot learning (FSL) papers mentioned in our FSL survey.
Solution for projects in Udacity course: Machine Learning Devops Engineer (MLOps)
Repository Nghiên cứu khoa học
Gathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 < Tensorflow < 2.0
The optimization methods in deep learning explained by Vietnamese such as gradient descent, momentum, NAG, AdaGrad, Adadelta, RMSProp, Adam, Adamax, Nadam, AMSGrad.
Pagerank algorithm and report
Build and install parallel algorithms on graphs
Computer Vision: Pedestrians Detection using HOG
POS problem with BiLSTM algorithm on Tensorflow 1.xx, language: Vietnamese , backend web with Flask
Project 3 on MLops Udacity: Deploying a Machine Learning Model on Heroku with FastAPI
Build your neural network easy and fast
Quản lý hiệu thuốc C# wpf
Similar question search system in the medical field.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)