Machine Learning Computer Vision

Linh Vo.V

**********@*****.*** 070******* Ho Chi Minh

ABOUT ME

UTE ’20. Student. My interests include

software design and development,

artificial intelligence, machine

learning and computer vision.

EDUCATION

2020

HCMC University of Technology and

Education

2020 - Now

PERSONAL INFORMATION

Github-alt github

LINKEDIN-IN linkedin

LINK website

TECHNICAL SKILLS

Python, Pytorch, OpenMMLab,

DeepStream, Docker.

CERTIFICATIONS

Analyze Datasets and Train ML Models using AutoML - 2023/02 From DeepLearning.AI

Software Development Processes and Methodologies - 2023/02 From University of Minnesota

Machine Learning Specialization - 2022/09

From DeepLearning.AI

Deep Learning Specialization - 2022/01

From DeepLearning.AI

Google Cloud Fundamentals for AWS Professionals - 2022 From Google Cloud

Machine Learning Engineering for Production (MLOps) - 2022 From DeepLearning.AI

IBM AI Engineering Professional Certificate - 2021 From IBM

Machine Learning with TensorFlow on Google Cloud - 2021 From Google Cloud

PERSONAL PROJECTS

Scraping weather data 2022

• Scraping data from a website for the weather and creating a new website visualize them by the chart.

• Use Flask to build backend server.

• Use Docker to deploy the website to Google Cloud. Predict Price House InWA 2022

• Use ML algorithm predicts the price house base on the feature provided.

• Train machine learning model.

• Build interface by Gradio.

• Use Flask to build backend server.

• Use Docker to deploy to Google Cloud.

Handwritten Math Symbols Classifier 2022

• Classifier handwritten math symbols. It includes basic Greek alphabet symbols like alpha, beta, gamma, mu, sigma, phi and theta.

• Build model mobilenet_v3 and use model training in the dataset.

• Optimize model.

Face Recognition 2021

• Training model mobilenet with softmax loss and triple loss on Casia-WebFace dataset.

• Compare accuracy when using softmax loss and triple loss.

• Optimize model.

Build a SageMaker Pipeline to train and deploy a BERT-Based text classifier 2021

• Define and run a pipeline using a directed acyclic graph (DAG) with specific pipeline parameters and model hyper-parameters.

• Define a processing step that cleans, balances, transforms, and splits our dataset into train, validation, and test dataset.

• Define a training step that trains a model using the train and validation datasets.

• Define a processing step that evaluates the trained model’s performance on the test dataset.

• Define a register model step that creates a model package from the trained model.

• Define a conditional step that checks the model’s performance and conditionally registers the model for deployment

Last updated April 5, 2023

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