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[한국소성가공학회] 재료와 공정 인공지능
| Dates | Topics | Jupyter notebook | Slides | |
| Python Installation Docker Installation (optional) |
iNote#00_1 iNote#00_2 |
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| 12/04/20 | Linear Algebra | iNote#01 | pdf#01 | |
| Optimization | iNote#02 | pdf#02 | ||
| Regression | iNote#03 | pdf#03 | ||
| Classification | iNote#04 | pdf#04 | ||
| Artificial Neural Networks | iNote#05 | pdf#05 | ||
| Convolutional Neural Networks (CNN) | iNote#06 | pdf#06 |
[대한금속·재료학회] 인공지능재료과학 분과 2020 하계단기강좌 “딥러닝의 기초이론과 재료설계 및 공정 최적화에 응용” [YouTube]
| Dates | Topics | Jupyter notebook | Slides | |
| 07/01/20 | Python Installation Docker Installation (optional) |
iNote#00_1 iNote#00_2 |
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| Introduction | pdf#00 | |||
| Linear Algebra | iNote#01 | pdf#01 | ||
| Optimization and Gradient Descent | iNote#02 | pdf#02 | ||
| Regression | iNote#03 | pdf#03 | ||
| Classification | iNote#04 | pdf#04 | ||
| 07/02/20 | Artificial Neural Networks | iNote#05 | pdf#05 | |
| Autoencoder | iNote#06 | pdf#06 | ||
| Convolutional Neural Networks (CNN) | iNote#07 | pdf#07 | ||
| Generative Adversarial Networks (GAN) | iNote#08 | pdf#08 |
[한국소음진동공학회] E-Conference Tutorial 강연 [YouTube]
| Dates | Topics | Jupyter notebook | Slides | |
| Docker Installation | iNote#00 | |||
| Introduction | pdf#01 | |||
| 신호처리 (FFT and STFT) | iNote#02 | pdf#02 | ||
| 진동, 열, 음향신호 분석을 위한 합성곱 신경망 (CNN) | iNote#03 | pdf#03 | ||
| 설명가능한 인공지능 (CAM) | iNote#04 | pdf#04 | ||
| 진동신호 분석을 위한 순환 신경망 (RNN) | iNote#05 | pdf#05 | ||
| 진동신호 생성을 위한 적대적 생성 신경망 (GAN) | iNote#06 | pdf#06 |
[internoise2020] Deep Learning for Noise/Vibration Engineering
Deep learning, considered as one of the breakthrough technologies in machine learning in recent years, has attracted tremendous research attention in both academia and industrial communities. It involves learning good representations of data through multiple levels of abstraction and can discover complicated underlying structures and features, thus achieving improved predictive performance. As a result, the noise and vibration community also starts to apply deep learning technologies to their applications. In this tutorial, I will first provide an overview of deep learning technology including ANN, CNN, RNN, and Autoencoder. In particular, the basic python codes with the TensorFlow library will be shared and explained during the tutorial. This tutorial can help more researchers in this community to understand the philosophy of deep learning and to utilize the provided codes in their practices. Ultimately I hope this tutorial can stimulate more research interests towards deep learning technology within our community.
– Deep learning (ANN, CNN, RNN, Autoencoder)
– Demo with vibration data from rotating machinery
– Python codes with TensorFlow will be provided.
| Dates | Topics | Jupyter notebook | Slides | Files or Data | |
| 08/23/20 | Python Installation | ||||
| Linear Algebra | iNote#01 | pdf#01 | |||
| Optimization and Gradient Descent | iNote#02 | pdf#02 | |||
| Regression | iNote#03_1 iNote#03_2 |
pdf#03 | |||
| Classification: Perceptron Classification: SVM Classification: Logistic Regression |
iNote#04 iNote#05 iNote#06 |
pdf#04 pdf#05 pdf#06 |
|||
| Artificial Neural Networks | iNote#07 | pdf#07 | |||
| Autoencoder | iNote#08 | pdf#08 | |||
| Convolutional Neural Networks (CNN) | iNote#09 | pdf#09 | |||
| Generative Adversarial Networks (GAN) | iNote#10 | pdf#10 |