吳珍儀,友達AI數位化打造高效供應鏈 蟬聯美國製造領導獎,Yahoo 財經,2023年6月29日
友達在智慧研發環節開發色彩飽和度模擬系統,引入大數據分析演算法,進行材料選用的相關模擬,提前預測客戶喜好的顏色和材料穿透率,使設計階段效率提升83%。在面板前段製程中,建立元件標準化資料庫並結合佈局設計演算法,成功縮短50%的光罩設計時程。
吳珍儀,友達AI數位化打造高效供應鏈 蟬聯美國製造領導獎,Yahoo 財經,2023年6月29日
友達在智慧研發環節開發色彩飽和度模擬系統,引入大數據分析演算法,進行材料選用的相關模擬,提前預測客戶喜好的顏色和材料穿透率,使設計階段效率提升83%。在面板前段製程中,建立元件標準化資料庫並結合佈局設計演算法,成功縮短50%的光罩設計時程。
Haifeng Jin, François Chollet, Qingquan Song, and Xia Hu. "AutoKeras: An AutoML Library for Deep Learning." the Journal of Machine Learning Research 6 (2023): 1-6. (Download)
To use deep learning, one needs to be familiar with various software tools like TensorFlow or Keras, as well as various model architecture and optimization best practices. Despite recent progress in software usability, deep learning remains a highly specialized occupation. To enable people with limited machine learning and programming experience to adopt deep learning, we developed AutoKeras, an Automated Machine Learning (AutoML) library that automates the process of model selection and hyperparameter tuning. AutoKeras encapsulates the complex process of building and training deep neural networks into a very simple and accessible interface, which enables novice users to solve standard machine learning problems with a few lines of code. Designed with practical applications in mind, AutoKeras is built on top of Keras and TensorFlow, and all AutoKeras-created models can be easily exported and deployed with the help of the TensorFlow ecosystem tooling.