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層 (深度學習)

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,或層次,是深度學習模型模型架構中的一種結構或網路拓撲,它從上一層獲取信息,然後將信息傳遞給下一層。深度學習中有幾個著名的層,即卷積神經網絡中的卷積層[1]和最大池化層[2][3]。基本神經網絡中的全連接層和ReLU層。循環神經網路中的RNN[4][5][6]自動編碼器中的解卷積層等。

與新皮質層次的相異

深度學習新皮質的分層方式有本質上的分別:深度學習的分層取決於網路拓撲新皮質的分層取決於層內的同質性

參見

參考文獻

  1. ^ Habibi, Aghdam, Hamed. Guide to convolutional neural networks : a practical application to traffic-sign detection and classification. Heravi, Elnaz Jahani. Cham, Switzerland. 2017-05-30. ISBN 9783319575490. OCLC 987790957. 
  2. ^ Yamaguchi, Kouichi; Sakamoto, Kenji; Akabane, Toshio; Fujimoto, Yoshiji. A Neural Network for Speaker-Independent Isolated Word Recognition. First International Conference on Spoken Language Processing (ICSLP 90). Kobe, Japan. November 1990 [2021-02-13]. (原始內容存檔於2021-03-07). 
  3. ^ Ciresan, Dan; Meier, Ueli; Schmidhuber, Jürgen. Multi-column deep neural networks for image classification. New York, NY: Institute of Electrical and Electronics Engineers (IEEE). June 2012: 3642–3649. CiteSeerX 10.1.1.300.3283可免費查閱. ISBN 978-1-4673-1226-4. OCLC 812295155. S2CID 2161592. arXiv:1202.2745可免費查閱. doi:10.1109/CVPR.2012.6248110.  |journal=被忽略 (幫助)
  4. ^ Dupond, Samuel. A thorough review on the current advance of neural network structures.. Annual Reviews in Control. 2019, 14: 200–230 [2021-02-13]. (原始內容存檔於2020-06-03). 
  5. ^ Abiodun, Oludare Isaac; Jantan, Aman; Omolara, Abiodun Esther; Dada, Kemi Victoria; Mohamed, Nachaat Abdelatif; Arshad, Humaira. State-of-the-art in artificial neural network applications: A survey. Heliyon. 2018-11-01, 4 (11): e00938. ISSN 2405-8440. PMC 6260436可免費查閱. PMID 30519653. doi:10.1016/j.heliyon.2018.e00938可免費查閱 (英語). 
  6. ^ Tealab, Ahmed. Time series forecasting using artificial neural networks methodologies: A systematic review. Future Computing and Informatics Journal. 2018-12-01, 3 (2): 334–340 [2021-02-13]. ISSN 2314-7288. doi:10.1016/j.fcij.2018.10.003可免費查閱. (原始內容存檔於2021-11-29) (英語).