/explore
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sander.ai | ||
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michael-lewis.com
This is a short summary of some of the terminology used in machine learning, with an emphasis on neural networks. I've put it together primarily to help my own understanding, phrasing it largely in non-mathematical terms. As such it may be of use to others who come from more of a programming than a mathematical background. |
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vankessel.io
A blog for my thoughts. Mostly philosophy, math, and programming. |
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www.khanna.law
You want to train a deep neural network. You have the data. It's labeled and wrangled into a useful format. What do you do now? |
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vxlabs.com
I have recently become fascinated with (Variational) Autoencoders and with PyTorch. Kevin Frans has a beautiful blog post online explaining variational autoencoders, with examples in TensorFlow and, importantly, with cat pictures. Jaan Altosaar's blog post takes an even deeper look at VAEs from both the deep learning perspective and the perspective of graphical models. Both of these posts, as well as Diederik Kingma's original 2014 paper Auto-Encoding Variational Bayes, are more than worth your time. |
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