在这篇文章中，我跳过了部分概念的重要细节，以促进理解。为了全面理解多层感知器，我推荐阅读斯坦福神经网络教程的第一、第二、第三和案例研究部分。如果有任何问题或者建议，请在下方评论告诉我。

第一：

第二：

第三：

案例研究：

参考文献

　　1. Artificial Neuron Models (https://www.willamette.edu/~gorr/classes/cs449/ann-overview.html)

　　2. Neural Networks Part 1: Setting up the Architecture (Stanford CNN Tutorial) ()

　　3. Wikipedia article on Feed Forward Neural Network (https://en.wikipedia.org/wiki/Feedforward_neural_network)

　　4. Wikipedia article on Perceptron (https://en.wikipedia.org/wiki/Perceptron)

　　5. Single-layer Neural Networks (Perceptrons) (~humphrys/Notes/Neural/single.neural.html)

　　6. Single Layer Perceptrons ()

　　7. Weighted Networks – The Perceptron ()

　　8. Neural network models (supervised) (scikit learn documentation) ()

　　9. What does the hidden layer in a neural network compute? ()

　　10. How to choose the number of hidden layers and nodes in a feedforward neural network? ()

　　11. Crash Introduction to Artificial Neural Networks (~iag/CS/Intro-to-ANN.html)

　　12. Why the BIAS is necessary in ANN? Should we have separate BIAS for each layer? ()

　　13. Basic Neural Network Tutorial – Theory (https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/)

　　14. Neural Networks Demystified (Video Series): Part 1, Welch Labs @ MLconf SF (https://www.youtube.com/watch?v=5MXp9UUkSmc)

　　15. A. W. Harley, "An Interactive Node-Link Visualization of Convolutional Neural Networks," in ISVC, pages 867-877, 2015 (link (~aharley/vis/harley_vis_isvc15.pdf))

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