AI Engineer
Resume:
P E R S O N A L
P R O F I L E
I am a Production Engineer -
Software Developer, looking
for a position in Machine
learning. I'm a strong team
player who is able to quickly
learn and apply new
technologies as well as
extremely motivated to
develop my skills and grow
professionally.
Ho Chi Minh University of
Technology
Bachelor in Electrical and
Electronic, 2021
Ms in Computer Science - 1'st
year
IELTS 6.0 - 2017
Deep learning Coursera -
Andrew Ng
Tensorflow developer Coursera
MLOPs Coursera
E D U C A T I O N &
C E R T I F I C A T I O N
Tran Van Hoang, Tan
Binh, HCM
dinhnguyenhuan2021@gmail.
com
/huan-dinh-nguyen-3aa2b21b5
C O N T A C T
Good understanding about deep learning, machine
learning, and deep learning models, MLOPs
TensorFlow, Pytorch, Scikit-learn, Tensorflow
Graphics,
Python, SQL, Git, Docker, Scrapy
Numpy, Pandas, Matplotlib, OpenCv, Web Crawling
S K I L L S
FPT Software, Data engineer fresher
Defines and builds data pipelines
Identify, analyze, and interpret trends or patterns in complex data sets
03/ 2021 - 05/2021
W O R K E X P E R I E N C E
DINH NGUYEN HUAN
AI ENGINEER
Akselos Vietnam, Sofware engineer internship
3D segmentations based on Point cloud and mesh
attributes
Write technical Python scripts to automate
engineering work
08/2021-02/2022
Ho Chi Minh University of Technology, Project
Management
Graduation Thesis: Machine Translation using
Seq2Seq and Transformers
02/2021-08/2021
Akselos Vietnam, Production engineer Software
Developer
Develop Machine learning Model
Python developer
02/2022 - CURRENT
LONG CV
COMPUTER VISION
I. Side Projects
Machine Translation: Using Sequence to Sequence and Transformer Sentiment Analysis
Fine-Tuning BERT for Sequence-Level and Token-Level NATURAL LANGUAGE PROCESSCING
Face tracking and image manipulation: create classes called CaptureManager and WindowManager as high-level interfaces to I/O streams use an object-oriented style because it promotes modularity and extensibility.
Foreground detection with the GrabCut algorithm
Image segmentation with the Watershed algorithm
Using OpenCV to perform face detection, Swapping faces in the infrared Retrieving Images and Searching Using Image Descriptors: Detecting DoG features and extracting SIFT descriptors, Using ORB with FAST features and BRIEF descriptors Building Custom Object Detectors: HOG descriptors -> NMS -> SVMs -> Detecting people with HOG descriptors
Tracking Objects: Detecting moving objects with background subtraction, Tracking colorful objects using MeanShift and CamShift, Finding trends in motion using the Kalman filter, Tracking pedestrians
Camera Models and Augmented Reality
Crawling from many websites and save as json or csv WEB - CRAWLING
A lot of Side project is on: https://github.com/HuanDinh20 OTHERS
II. AI skills
ML MODEL
+ Linear regression, Logistic Regression, Support Vector Machine, K - Means Clustering, Decision Trees and Random Forests, Gradient Boost, AdaBoost, XGBoost, Principle Component Analysist DEEP LEARNING
Basic Neural Networks
Convolutional Neural Networks, Modern Convolutional Neural Networks: AlexNet, VGG, NiN, GoogleNet,ResNet, DenseNet
Recurrent Neural Networks, Modern Recurrent Neural Networks: GRU, LSTM, Bidirectional RNN, Encoder - Decoder, Sequence to Sequence Learning, Beam Search Attention Mechanisms: Bahdanau Attention, Transformer Optimization Algorithms: Gradient Descent, Stochastic Gradient Descent, Minibatch Stochastic Gradient Descent, Adagrad, RMSProp, Learning Rate Scheduling Pre-training:
Word Embedding (word2vec), Approximate Training, Word Embedding with Global Vectors (GloVe), Subword Embedding,
Word Similarity and Analogy, Bidirectional Encoder Representations from Transformers
(BERT)
Histograms, Binnings, and Density, Customizing Plot Legends, Colobars, Multiple Subplots, Three-Dimensional Plotting in Matplotlib
Matplotlib, Seaborn:
Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements
Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application Build data pipelines by gathering, cleaning, and validating datasets Implement feature engineering, transformation, and selection with TensorFlow Extended Apply techniques to manage modeling resources and best serve offline/online inference requests
NLP
VISUALIZATION
MLOPS
COMPUTER VISION
Image Augmentation, Fine-Tuning
Object Detection and Bounding Boxes
+ Anchor Boxes
+ Multiscale Object Detection
+ Region-based CNNs (R-CNNs)
Pre-training:
Sentiment Analysis: Using Recurrent Neural Networks, Sentiment Analysis: Using Convolutional Neural Networks, Natural Language Inference: Using Attention Fine-Tuning BERT for Sequence-Level and Token-Level Applications, Natural Language Inference: Fine-Tuning BERT
Use analytics to address model fairness, explain-ability issues, and mitigate bottlenecks Deliver deployment pipelines for model serving that require different infrastructures Apply best practices and progressive delivery techniques to maintain a continuously operating production system
Handling Files, Cameras, and GUIs
Processing Images: Converting images between different color models, Fourier transform, Contour detection, Detecting lines, circles, and other shapes Depth Estimation and Segmentation: Capturing frames from a depth camera, Converting 10-bit images to 8-bit, Creating a mask from a disparity map, Depth estimation with a normal camera, Retrieving Images and Searching Using Image Descriptors, Camera Models and Augmented Reality
OPENCV
Scrapy, Beautiful Soup, Selenium
HTML Parsing, Reading Documents: Text, CSV, PDF
III. Python Skills
WEB SCRAPING
Broadcasting, Comparisons, Masks, and Boolean Logic Fancy Indexing
Sorting Arrays
NumPy’s Structured Arrays
NUMPY
Handling Missing Data, Hierarchical Indexing
Combining Datasets: Concat and Append, Aggregation and Grouping Pivot Tables
Vectorized String Operations
Working with Time Series
PANDAS
Sofware Design Principles: Design Principles, SOLID Principles Creational Design Pattern:
Structural Design Patterns
Behavioral Design Patterns
DESIGN PATTERN
Basic Data Structures: Arrays, Linked Lists, Stacks, Queues, Tree Dynamic Arrays and Amortized Analysis: Aggregate Method, Banker's Method, Physicist's Method
Priority Queues and Disjoint Sets
Hash Tables
DATA STRUCTURES AND ALGORITHMS
Data Structures
Algorithms on Graphs:
Algorithms on Strings
Algorithms
IV. Database System
SQL
Database Design: Database Design Using the E-R Model, Relational Database Design Application and Development: Complex Data Types, Application Development Big Data Analytics
Storage Management and Indexing: Physical Storage Systems, Data Storage Structures, Indexing
Query Processing and Optimization
Transaction Management
Paralel and Distributed Database
V. Docker
The Docker Engine
Images
Containers
Containerizing an app
Deploying Apps with Docker Compose
Docker Swarm
Docker Networking
Docker overlay networking
Volumes and persistent data
Volumes and persistent data
Volumes and persistent data
VI. Git
Git Basic:
Getting a Git Repository: Initializing a Repository in an Existing Directory, Cloning an Existing Repository
Recording Changes to the Repository: Checking the Status, Tracking New Files, Staging Modified Files, Ignoring Files, Viewing Staged and Unstaged Changes, Committing Changes, Skipping the Staging Area, Removing Files, Moving Files Viewing the Commit History, Limiting Log Output
Undoing Things: Unstaging a Staged File, Unmodifying a Modified File, Undoing things with git restore
Working with Remotes: Showing Remotes, Adding Remote Repositories, Fetching and Pullin, Pushing to Remotes, Inspecting a Remote, Renaming and Removing Remotes Tagging
Git Aliases
Basic Branching and Merging
Branch Management: Changing a branch name, master branch name Branching Workflows: Long-Running Branches, Topic Branches, Remote Branches
Rebasing
Git Branching
The Protocols: local protocol, HTTP Protocols, SSH Protocol, Git Protocol Getting Git on a Serve, Putting the Bare Repository on a Serve Generating SSH Public Key
Setting Up the Server
Git Daemon
Smart HTTP
Git on the Server
Distributed Workflows: Centralized Workflow, Integration-Manager Workflow, Dictator and Lieutenants Workflow
Contributing to a Project
Maintaining a Project
Distributed Git
Contact this candidate