Top 3 Jupyter Notebook gradient-descent Projects
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is used to capture the power of a fully-trained deep net of infinite width.
https://openreview.net/pdf?id=rkl4aESeUH, https://github.com/google/neural-tangents
> It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width.
https://arxiv.org/abs/1711.00165
And of course, one needs to look back at SVMs applying a kernel function and separating with a line, which looks a lot like an ANN with a single hidden layer followed by a linear mapping.
https://stats.stackexchange.com/questions/238635/kernel-meth...
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Jupyter Notebook gradient-descent related posts
Index
What are some of the best open-source gradient-descent projects in Jupyter Notebook? This list will help you:
Project | Stars | |
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1 | neural-tangents | 2,217 |
2 | Intro-to-Linear-Regression-and-Gradient-Descent | 3 |
3 | MLConceptsStudy | 2 |