歡迎報名參加清大計通中心舉辦之113年度寒假MATLAB系列課程,請選擇最適合您的內容報名參加。

報名前請確認可全程參與較佳，報名請至 https://forms.gle/QcvYvY3imyGPth4A7

利用MATLAB於GPU進行影像處理/電腦視覺之深度學習應用

深度學習技術近年來發展越趨成熟，也在語音辨識及影像辨識應用上有不錯的表現。本課程以影像辨識應用為例，展示MATLAB如何快速開發具有影像辨識能力的深度學習模型，並搭配使用GPU加速模型訓練。

• What is Deep Learning?
• Layers in Convolution Neural Network
• Image classification using a pre-trained network
• Train a new model
• Object detection

使用統計與機器學習方法於資料分析的應用

本課程介紹MATLAB於資料分析的相關功能，主要目的為闡述如何使用MATLAB補強Excel的不足之處。在課程中將會說明如何使用MATLAB匯入Excel的資料、視覺化分析以及客製化圖形、進行統計分析與數學模型的配飾、自動化流程並產生報表、最後將MATLAB開發出的功能包裝成Excel add-in。

• Access data from files and Excel spreadsheets
• Visualize data and customize figures
• Perform statistical analysis and fitting
• Generate reports and automate workflows
• Share analysis tools as standalone applications or Excel add-ins

不是人工智慧專家也能上手-AI低程式碼開發MATLAB app

"第一階段介紹怎麼在MATLAB中快速實現深度學習影像處理分類與遷移式學習，甚至可以不需要寫程式碼即可訓練自己的分類模型。
第二部分深入介紹物件偵測、語義分割、深度學習視覺化、異常偵測等進階算法。
第三部分介紹如何跟其他Pytorch與Tensorflow做模型整合，以及將MATLAB與其他環境的整合。

• Classification
• - How to use Pretrained model
• - Create Deep Learning Model(MNIST)
• - Try to do Transfer Learning
• Object Detection & Advance
• - Object Detection(YOLO)
• - Semantic Segmentation
• - Deep Learning Visualization
• - Anomaly detection
• Integrate
• - Deep Learning Model Exchange
• - Application And Deployment"

※Please note: This workshop will be conducted in Mandarin※

Welcome to register for the 2024 Winter Break MATLAB Series Courses hosted by NTHU Computing and Communication Center. Please choose the content that suits you best.

Before registering, please ensure full attendance. To register, please visit https://forms.gle/QcvYvY3imyGPth4A7

• Class Venue: Teams
• Contact: inquiry@cc.nthu.edu.tw
• Campus Extension: 31231
• Contact Person: Miss Chiang

Any inquiries,
please mail to the following address: inquiry@cc.nthu.edu.tw or call Miss Chiang at campus ext.31231

Utilizing MATLAB for GPU-based Image Processing/Computer Vision Deep Learning Applications

In recent years, deep learning technology has matured and shown excellent performance in applications such as speech and image recognition. This course focuses on image recognition applications, demonstrating how MATLAB can rapidly develop deep learning models with image recognition capabilities and accelerate model training using GPU.

• What is Deep Learning?
• Layers in Convolution Neural Network
• Image classification using a pre-trained network
• Train a new model
• Object detection

Application of Statistical and Machine Learning Methods in Data Analysis

This course introduces MATLAB's functionalities related to data analysis, aiming to elucidate how to use MATLAB to supplement the limitations of Excel. The course will cover importing data from Excel, visualizing analysis, customizing graphics, statistical analysis, mathematical model embellishments, automating processes, generating reports, and packaging MATLAB-developed functions into Excel add-ins.

• Access data from files and Excel spreadsheets
• Visualize data and customize figures
• Perform statistical analysis and fitting
• Generate reports and automate workflows
• Share analysis tools as standalone applications or Excel add-ins

AI Low-Code Development – MATLAB App for Non-AI Experts

The first stage introduces how to quickly implement deep learning image processing classification and transfer learning in MATLAB, even without coding to train your classification model. The second part delves into advanced algorithms such as object detection, semantic segmentation, deep learning visualization, and anomaly detection. The third part introduces how to integrate with other PyTorch and TensorFlow models and integrate MATLAB with other environments.

• Classification
• How to use Pretrained model
• Create Deep Learning Model (MNIST)
• Try to do Transfer Learning
• Object Detection & Advance
• Object Detection (YOLO)
• Semantic Segmentation
• Deep Learning Visualization
• Anomaly detection
• Integrate
• Deep Learning Model Exchange
• Application And Deployment