neural-tangents
pymc-resources
neural-tangents | pymc-resources | |
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4 | 5 | |
2,225 | 1,888 | |
0.6% | 1.0% | |
7.6 | 3.7 | |
2 months ago | 4 months ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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neural-tangents
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Any Deep ReLU Network Is Shallow
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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[R] Training Machine Learning Models More Efficiently with Dataset Distillation
Code for https://arxiv.org/abs/2011.00050 found: https://github.com/google/neural-tangents
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[D] Relationship Between Kernels, Neural Networks and Gaussian Process
I saw that you asked about neural tangent kernels (NTK) in another post yesterday -- be aware that what you're referencing in the present post are "neural network gaussian processes" (NNGP), which is distinct from NTK! The README of https://github.com/google/neural-tangents should help lift confusion. (I also took the term NNGP from there.)
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[D] neural tangent kernel
It's true! There have been dozens of papers published on this topic, some of which are listed here: https://github.com/google/neural-tangents#references
pymc-resources
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Bayesian Analysis with Python
As it happens, there's a PyMC implementation of the 1st and 2nd editions of Statistical Rethinking here:
https://github.com/pymc-devs/pymc-resources
(I think the author of the book discussed above, Osvaldo Martin, is the primary or sole contributor for the Rethinking implementations, in fact -- he had a full implementation in his own repo [here](https://github.com/aloctavodia/Statistical-Rethinking-with-P...) before deprecating it in favor of the above-linked one.)
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Predicting the distribution of a variable rather than a point estimate
That course/book has been translated to Python (using PyMC3 for the modeling, but you could also use the Stan examples and run them from Python using CmdStanPy).
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Statistical Rethinking (2022 Edition)
Prof. McElreath has been adding two new videos every week.
Also, for anyone who prefers to use the pythons for the coding, I recommend the PyMC3 notebooks https://github.com/pymc-devs/resources/tree/master/Rethinkin... There is also a discussion forum related to this repo here https://gitter.im/Statistical-Rethinking-with-Python-and-PyM...
- Statistical rethinking, but with examples in python?
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Stan is a state-of-the-art platform for statistical modeling
The Statistical Rethinking book uses R.
For people wanting Python, Jupyter notebooks with Python code examples are here:
* https://github.com/pymc-devs/resources/tree/master/Rethinkin...
What are some alternatives?
eigenlearning - codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"
indaba-pracs-2022 - Notebooks for the Practicals at the Deep Learning Indaba 2022.
mango - Parallel Hyperparameter Tuning in Python
skbel - SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
cookiecutter-pystan
Bayesian-Optimization-in-FSharp - Bayesian Optimization via Gaussian Processes in F#
hyper-nn - Easy Hypernetworks in Pytorch and Jax
timm-vis - Visualizer for PyTorch image models
google-research - Google Research