AI Engineer

CÔNG TY TNHH GIẢI PHÁP THỊ GIÁC MÁY TÍNH **/**** - 12/2021

Hồ sơ được tạo tự động bởi TopDev - Việc làm IT hàng đầu TRAN LUAT VY

DATA ANALYST

GENERAL INFORMATION

Email ********@*******.*****.***.**

Mobile phone 034*******

Gender MALE

Date of birth 13/07/2000

Location Cho Moi, An Giang, Tỉnh An Giang

Github https://github.com/luatvy

SUMMARY

I am a student at University of Science.

I am writing to you with regards to the Data Analyst position. Motivated college student with good knowledge in Mathematics and Computer Science. Having 2 years of programming experience in solving problems from data structure, algorithms to statistical analysis. I have studied Machine Learning, Deep Learning, Basic Big Data. Looking for Data Analyst position to leverage my skills and gain more practical work experience. WORK EXPERIENCE

AI INTERN

Responsibilities

- Data collection.

- Image labeling.

- Training model.

- Deploy model.

TECHNICAL SKILLS

AI / Machine Learning/ Big Data: Python, C/C++, My SQL, PyTorch, Tensor ow, PySpark. Other skills: Git, Django, Flask, Tableau.

EDUCATION

Vietnam National University Ho Chi Minh City - University of Science 08/2018 - 08/2022 Major: Data Science

PROJECTS

Project: Car Price Prediction (Class project)

Description:

Prediction of car prices from lots of features such as: brand, length, height, number of doors, pay load, ... Drawl data from www.cars-data.com: using HTMLSession and JSON. Preprocessing data: one hot encoding, ll missing values, standardscaler. Model: MLPRegressor.

Github: https://github.com/huasen07/Data_Science_Final_Project Project: Flower classi cation

Description:

Flower classi cation from image:

Drawl owers image from Internet.

Image labeling.

Using model to classify owers image.

Deploy model using Django.

The client uploads an image and this image is sent to the server. The server will predict the image and return the answer back to the client. Project: Global Wheat Detection Kaggle (Class project) Description:

Global Wheat Detection Kaggle: using Global Wheat Head dataset to pretrain Faster R-CNN model. After that, we can use that model to detect wheat head.

Summary:

Data Cleaning: delete tiny bounding boxes (width or height < 10px). Augmentation: RandomCrop, HorizontalFlip, VerticalFlip, ToGray, GaussNoise, Blur, RandomBrightnessContrast, HueSaturationValue, Mixup.

Model: Faster R-CNN (backbone: resnet-50, resnet-101, resnet-152). Ensemble multi-scale model: using Weighted-Boxes-Fusion. Test time augmentation: HorizontalFlip, VerticalFlip, Rotate90. Pseudo labeling.

Final score(mAP):

Public leaderboard: 0.7343.

Private leaderboard: 0.6323.

Github: https://github.com/luatvy/Global-Wheat-Detection HOBBIES

Football, Tourism, Games.

Contact this candidate