Why Do Neural Networks Love the Softmax?

The machine learning consultancy: https://truetheta.io Join my email list to get educational and useful articles (and nothing else!): https://mailchi.mp/truetheta/true-theta-email-list Want to work together? See here: https://truetheta.io/about/#want-to-work-together Neural Networks see something special in the softmax function. SOCIAL MEDIA LinkedIn : https://www.linkedin.com/in/dj-rich-90b91753/ Twitter : https://twitter.com/DuaneJRich Github: https://github.com/Duane321 Enjoy learning this way? Want me to make more videos? Consider supporting me on Patreon: https://www.patreon.com/MutualInformation SOURCE NOTES I decided to make this video when inspecting jacobians/gradients starting from the end of a small network. Right near the softmax, the jacobian looked simple enough that I suspected interesting math behind it. And there was. I came across several excellent blogs on the Softmax's jacobian and its interaction with the negative log likelihood. Source [1] was the primary source, since it was quite well explained and used condensed notation. [2] was useful for understanding the broader context and [3] was a separate, thorough perspective. SOURCES [1] M. Peterson, "Softmax with cross-entropy," https://mattpetersen.github.io/softmax-with-cross-entropy, 2017 [2] I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016, section 6.2.2.3 [3] M. Lester James, "Understanding softmax and the negative log-likelihood," https://ljvmiranda921.github.io/notebook/2017/08/13/softmax-and-the-negative-log-likelihood/, 2017 TIME CODES 0:00 Everyone uses the softmax 0:23 A Standard Explanation 3:20 But Why the Exponential Function? 3:57 The Broader Context 6:05 Two Choices Together 6:51 The Gradient 10:07 Other Reasons