2019年3月13日 · One of my favorite books on theoretical aspects of neural networks is Anthony and Bartlett's book: "Neural Network Learning Theoretical Foundations". This book studies neural networks in the context of statistical learning theory. You will find loads of estimates of VC dimensions of sets of networks and all that fun stuff.

2016年5月3日 · A good book on convolutional neural networks. Ask Question Asked 10 years, 8 months ago.

2017年11月12日 · Rather than the chain rule, let's tackle the problem using differentials. Let's use the convention that an upppercase letter is a matrix, lowercase is a column vector, and a greek letter is a scalar.

2018年3月27日 · In your neural networks application, the optimisation algorithm always wants to go in the direction where the cost decreases the most. The algorithm doesn't see the cost globally, only in single points, it has to make a local decision, and going down the steepest slope is the best one can do.

2020年6月10日 · neural-networks; Share. Cite. Follow edited Jul 19, 2020 at 19:24. Taylor Rendon. 2,315 2 2 gold badges 12 ...

$\begingroup$ Any book on neural networks will deal with the sigmoid function. It is useful because of the simple way backpropagation works; a lot of computing work is saved when training a network from a set of results.

$\begingroup$ @ABC, Langlands isn't really a grand unified theory of mathematics - that's just something Edward Frenkel said to convey the importance of the work to convey the importance of the program to the interested non-expert.

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Hyperbolic trigonometric functions have real-world applications in physics, engineering, and mathematics, including modeling hyperbolas and solving differential equations.

2017年12月14日 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.