Machine Learning Software Engineering
Resume:
PROFILE INFO
Machine Learning
Engineer intern
NGUYỄN TOẢN
EDUCATION
SKILLS
LANGUAGES
SOFTWARE ENGINEER
GIADINH UNIVERSITY
CONTACT
EXPERIENCE
As a final-year undergraduate student at Gia Dinh
University, I am pursuing a degree in information
technology, particularly in machine learning. I have actively engaged in developing programs such as Brain Tumor Segmentation and MNIST Handwritten Digit Classification. My hands-on experience has been centered around
building and implementing solutions within the realm of machine learning. Currently seeking an opportunity to embark on a challenging career, I am eager to apply my skills innovatively and straightforwardly. I thrive in collaborative team environments and take pleasure in communicating data-driven results.
NEXT SENTENCE PREDICTION
USING BERT
In the "Next Sentence Prediction" project, I was the main developer. Using BERT, we predicted the next
sentences. This improved context and semantic
understanding. Applications include machine
translation, chatbots, and sentiment analysis. My role enhanced my data analysis skills.
In the project, we developed a system to identify
tumor-affected brain areas. I contributed to the
research and development of segmentation
models. We used TensorFlow and Keras to build a U- Net-based model, applying preprocessing
techniques. The model achieved over 90% accuracy,
aiding physicians in diagnosis and monitoring,
potentially improving patient treatment outcomes.
Algorithms: Linear, logistic,
decision trees, random
forests, LSTM.
Libraries and Frameworks:
Keras, TensorFlow, PyTorch.
Data tools: NumPy, pandas.
Programming Languages:
Python, C++.
Evaluation and Validation:
Cross-validation, accuracy,
precision.
Vietnamese
English (basic)
43/14/20-Cong Hoa-Tan
Binh- Ho Chi Minh city
vantoan2905
************@*****.***
2023
2023
2021 - 2024
BRAIN TUMOR SEGMENTATION
The project focused on implementing a face
recognition system using EfficientNetV2B0
architecture, a deep learning approach.
Responsibilities included researching, optimizing, and integrating the model for real-time applications. TensorFlow and Keras were utilized for model
development, incorporating techniques like transfer learning and data preprocessing. The system
achieved high accuracy in identifying faces,
showcasing its potential for enhancing security and authentication systems.
COURSE FACE RECOGNITION 2024
Dive into deep learning:
Interactive deep learning
course with code, math,
and discussions
Contact this candidate