深度學習

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深度學習嘅圖例;個神經網絡一層層噉將訊息抽象化。

深度學習粵拼sam1 dou6 hok6 zaap6英文deep learning)係基於人工神經網絡嘅一種機械學習做法。定義上,深度學習係一類機械學習演算法,特徵係分做多層,每一層都負責由輸入嗰度抽取比前一層高層次(high-level)嘅特徵。舉個簡單例子說明,想像有個用嚟處理動物影像嘅人工神經網絡,佢第一層會接收幅圖嘅線條,第二層會按線條留意幅圖有邊啲部位(例:如果有兩條打戙嘅線,嗰個部位可能就係一條),第三層會留意部份之間嘅相對位置(例:幅圖隻動物有四隻髀,所以應該唔會係昆蟲),第四層會將所有部份一齊考慮,砌出一個心目代表隻動物抽象化之後嘅樣,最後第五層就會計算抽象化之後個樣最接近邊種已知嘅動物,並且俾返一個標記佢(例:「隻動物係大笨象」)[1][2][3]

喺廿一世紀,深度學習有以下嘅應用,而且喺某啲情況當中,深度學習嘅表現仲好過人類[4][5]

... 等等。

睇埋[編輯]

參考書[編輯]

  • Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). Deep Learning. MIT Press. ISBN 978-0-26203561-3.

[編輯]

  1. Deng, L.; Yu, D. (2014). "Deep Learning: Methods and Applications". Foundations and Trends in Signal Processing. 7 (3–4): 1–199.
  2. Bengio, Y.; Courville, A.; Vincent, P. (2013). "Representation Learning: A Review and New Perspectives". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (8): 1798–1828.
  3. Schmidhuber, J. (2015). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117.
  4. Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffry (2012). "ImageNet Classification with Deep Convolutional Neural Networks" (PDF). NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada.
  5. Marblestone, Adam H.; Wayne, Greg; Kording, Konrad P. (2016). "Toward an Integration of Deep Learning and Neuroscience". Frontiers in Computational Neuroscience. 10: 94.
  6. Ciresan, Dan; Meier, U.; Schmidhuber, J. (June 2012). "Multi-column deep neural networks for image classification". 2012 IEEE Conference on Computer Vision and Pattern Recognition: 3642–3649.
  7. Olshausen, B. A. (1996). "Emergence of simple-cell receptive field properties by learning a sparse code for natural images". Nature. 381 (6583): 607–609.

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