第十六章 典型网络结构

教学资料

• 教学参考
• 课件
• 小清爱提问：什么是卷积神经网络？[]
• 小清爱提问：什么是循环神经网络？[]

扩展阅读

• AI100问：什么是卷积神经网络？[1]
• AI100问：什么是循环神经网络？[2]
• AI100问：什么是自编码器？[3]
• 维基百科：多层感知器[4][5]
• 维基百科：卷积神经网络[6][7]
• 维基百科：循环神经网络[8][9]
• 维基百科：自编码器[10][11]
• 机器之心：卷积神经网络 [12]

视频展示

• 全连接网络展示 [13]
• 全连接层展示 [14]
• Disclaimer CNN 展示 [15]
• CNN_Otavio_Good 的CNN展示 [16]
• 知多少：什么是循环神经网络[17]

演示链接

• Andrej Karpathy's CNN demo [18]
• Neural Net demo [19]
• Neural Net training demo [20]
• Quick draw, and let NN guess [21]

开发者资源

• Python package for neural nets: PyTorch [22] TensorFlow[23] NeuralLab[24]

高级读者

• Fukushima, Kunihiko (1980). "Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position" [25]
• Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, Backpropagation Applied to Handwritten Zip Code Recognition; AT&T Bell Laboratories [26]
• Hopfield, J. J. (1982). "Neural networks and physical systems with emergent collective computational abilities". Proceedings of the National Academy of Sciences. 79 (8): 2554–2558. [27]
• Elman, Jeffrey L. (1990). "Finding Structure in Time". Cognitive Science. 14 (2): 179–211. [28]
• Jordan, Michael I. (1997-01-01). "Serial Order: A Parallel Distributed Processing Approach". Neural-Network Models of Cognition - Biobehavioral Foundations. Advances in Psychology. Neural-Network Models of Cognition. Vol. 121. pp. 471–495. [29]
• Hinton, G. E., & Zemel, R. S. (1994). Autoencoders, minimum description length and Helmholtz free energy. In Advances in neural information processing systems 6 (pp. 3-10). [30]
• 王东，机器学习导论,第三章，神经模型，2021,清华大学出版社 [31]
• Christopher M. Bishop, Neural Networks for Pattern Recognition [32]
• Christopher M. Bishop, Pattern Recognition and Machine Learning [33]