層 (深度學習)
層，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層[4][5][6]和自動編碼器中的解卷積層等。
與新皮質層次的相異
深度學習與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
- ^ 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.
- ^ 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）.
- ^ 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
. ISBN 978-1-4673-1226-4. OCLC 812295155. S2CID 2161592. arXiv:1202.2745 . doi:10.1109/CVPR.2012.6248110. |journal=被忽略 (幫助) - ^ 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）.
- ^ 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 . PMID 30519653. doi:10.1016/j.heliyon.2018.e00938 （英語）.
- ^ 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 . （原始內容存檔於2021-11-29） （英語）.
