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职场能力评价定义
Austin Yang
Hello, I am Ming-Hao, Yang.
I graduated from National Yunlin University of Science and Technology with M.S. in Computer Science and Information Engineering.
My research interests are in deep learning, speech synthesis, speech recognition, anomalous sound detection.
I am familiar with Linux, audio and computer vision, deep learning framework PyTorch, Keras, Tensorflow.
Machine Learning Engineer
Work Experience
National Yunlin University of Science and Technology, Machine Engineer, Feb 2020 ~ Present
I am responsible for solving industry issues, such as anomaly detection, object detection, object classification, and anomalous sound detection.
At the same time, I serve as the instructor of the Ministry of Education's online pre-employment training course to train AI-related talents.
This course teaches college students of AI-related technology across the country to introduce AI into enterprises and also sets up AI workshops to teach enterprises how to use AI to recognize environmental sounds.
Projects
- Respiration sound recognition
- Infant sound recognition
- Anomaly image recognition
- Anomalous sound detection
National Yunlin University of Science and Technology, Artificial Intelligence Engineer, Aug 2019 ~ Oct 2019
This project aims to use students' online learning behaviors to predict whether students will pass the course at the end of the semester and use the AI model to give appropriate learning suggestions.
In the project, I am responsible for the establishment of the entire project such as behavioral feature analysis, AI model establishment, and hyperparameter tuning and prediction.
Education
National Yunlin University of Science and Technology, Master’s Degree, Computer Science and Information Engineering, 2017 ~ 2019
National Yunlin University of Science and Technology, Bachelor’s Degree, Computer Science and Information Engineering, 2012 ~ 2017
Skills
Programming language
- Python
- Ruby
- C++
Machine learning
- SVM
- KNN
- PCA
- Random forest
- Decision tree
Deep learning
- Generative Adversarial Network
- Autoencoder
- Convolutional neural network
- Fully connected neural network
Speech
- Speech synthesis
- Speaker recognition
- Sound event classification
Computer vision
- Object detection
- Object classification
- Image classification
Tools
- git
- GitLab
- Docker
- Shell script
Portfolio
ImageClassification https://github.com/fastyangmh/ImageClassification
This repository is an image classification based on deep learning.
It contains various state-of-art models like ResNet, MobileNet, EfficientNet and the user can use a self-defined model.
This repository has a hyperparameter tuning and a k-fold cross validation feature.
You can easily use this repository to complete tasks of image classification.
What you need is to prepare data and input instructions according to the document, and then you can automatically start training the AI model, and finally, get the best AI model.
SoundClassification https://github.com/fastyangmh/SoundClassification
This repository is an sound classification based on deep learning.
It contains various state-of-art models like ResNet, MobileNet, EfficientNet and the user can use a self-defined model, also contains any audio transform based on SoX.
This repository has a hyperparameter tuning and a k-fold cross validation feature.
You can easily use this repository to complete tasks of sound classification.
What you need is to prepare data and input instructions according to the document, and then you can automatically start training the AI model, and finally, get the best AI model.
AudioGANomaly https://github.com/fastyangmh/AudioGANomaly
AudioGANomaly is based on an anomaly detection paper named, "GANomaly: Semi-Supervised Anomaly Detection via Adversarial Train", this architecture uses Encoder-Decoder-Encoder to learn the distribution of normal data in high dimensional space and combine with a classifier based on Encoder by adversarial learning.
AudioDenoiser https://github.com/fastyangmh/AudioDenoiser
AudioDenoiser is based on an anomaly detection paper named, "Real Time Speech Enhancement in the Waveform Domain", this architecture is similar to Autoencoder and U-Net and adds LSTM in the middle layer to improve denoising performance.
Master's Thesis
Austin Yang
Hello, I am Ming-Hao, Yang.
I graduated from National Yunlin University of Science and Technology with M.S. in Computer Science and Information Engineering.
My research interests are in deep learning, speech synthesis, speech recognition, anomalous sound detection.
I am familiar with Linux, audio and computer vision, deep learning framework PyTorch, Keras, Tensorflow.
Machine Learning Engineer
Work Experience
National Yunlin University of Science and Technology, Machine Engineer, Feb 2020 ~ Present
I am responsible for solving industry issues, such as anomaly detection, object detection, object classification, and anomalous sound detection.
At the same time, I serve as the instructor of the Ministry of Education's online pre-employment training course to train AI-related talents.
This course teaches college students of AI-related technology across the country to introduce AI into enterprises and also sets up AI workshops to teach enterprises how to use AI to recognize environmental sounds.
Projects
- Respiration sound recognition
- Infant sound recognition
- Anomaly image recognition
- Anomalous sound detection
National Yunlin University of Science and Technology, Artificial Intelligence Engineer, Aug 2019 ~ Oct 2019
This project aims to use students' online learning behaviors to predict whether students will pass the course at the end of the semester and use the AI model to give appropriate learning suggestions.
In the project, I am responsible for the establishment of the entire project such as behavioral feature analysis, AI model establishment, and hyperparameter tuning and prediction.
Education
National Yunlin University of Science and Technology, Master’s Degree, Computer Science and Information Engineering, 2017 ~ 2019
National Yunlin University of Science and Technology, Bachelor’s Degree, Computer Science and Information Engineering, 2012 ~ 2017
Skills
Programming language
- Python
- Ruby
- C++
Machine learning
- SVM
- KNN
- PCA
- Random forest
- Decision tree
Deep learning
- Generative Adversarial Network
- Autoencoder
- Convolutional neural network
- Fully connected neural network
Speech
- Speech synthesis
- Speaker recognition
- Sound event classification
Computer vision
- Object detection
- Object classification
- Image classification
Tools
- git
- GitLab
- Docker
- Shell script
Portfolio
ImageClassification https://github.com/fastyangmh/ImageClassification
This repository is an image classification based on deep learning.
It contains various state-of-art models like ResNet, MobileNet, EfficientNet and the user can use a self-defined model.
This repository has a hyperparameter tuning and a k-fold cross validation feature.
You can easily use this repository to complete tasks of image classification.
What you need is to prepare data and input instructions according to the document, and then you can automatically start training the AI model, and finally, get the best AI model.
SoundClassification https://github.com/fastyangmh/SoundClassification
This repository is an sound classification based on deep learning.
It contains various state-of-art models like ResNet, MobileNet, EfficientNet and the user can use a self-defined model, also contains any audio transform based on SoX.
This repository has a hyperparameter tuning and a k-fold cross validation feature.
You can easily use this repository to complete tasks of sound classification.
What you need is to prepare data and input instructions according to the document, and then you can automatically start training the AI model, and finally, get the best AI model.
AudioGANomaly https://github.com/fastyangmh/AudioGANomaly
AudioGANomaly is based on an anomaly detection paper named, "GANomaly: Semi-Supervised Anomaly Detection via Adversarial Train", this architecture uses Encoder-Decoder-Encoder to learn the distribution of normal data in high dimensional space and combine with a classifier based on Encoder by adversarial learning.
AudioDenoiser https://github.com/fastyangmh/AudioDenoiser
AudioDenoiser is based on an anomaly detection paper named, "Real Time Speech Enhancement in the Waveform Domain", this architecture is similar to Autoencoder and U-Net and adds LSTM in the middle layer to improve denoising performance.