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### Source
| **ZH** | **EN** |
| -------- | -------- |
| 1. [TensorFlow AMP](https://man.twcc.ai/@twccdocs/doc-ccs-main-zh/https%3A%2F%2Fman.twcc.ai%2F%40twccdocs%2FBJ-sMMc2r%3Ftype%3Dview) | […
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I have been thinking for a while to add some shortcut and stripped down linear models into regression.
Stripped down means that not all functionality and results are available, i.e. "limited" models.…
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What is the Python statsmodels equivalent for R step() function of stepwise regression with AIC as criteria?
I found a [stepwise regression with p-value as criteria](https://datascience.stackexchan…
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Hi @DilumAluthge , we currently have
```julia
m = @model X, s, t begin
p = size(X, 2) # number of features
β ~ Normal(0, s) |> iid(p) # coefficients
σ ~ HalfNormal(t) # dispersion
…
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Hello,
@hiroyuki-kasai Thank you for creating this project with a wide variety of algorithms.
I went through the code in linear_regression_data_generator.m and was not quite clear how the data …
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Hi,
I have successfully managed to clone and install your package, and I tried out SRTM method on my own data where as a reference mask I've used whole brain mask (for testing purposes).
Probl…
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I can see that Stochastic Gradient Descent has already been implemented. But linear regression works using simple gradient descent. What are the challenges to implementing SGD for Linear Regression.
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* Steps vs cost
* hyperparameters vs cost
## Reference
http://stackoverflow.com/questions/28542686/3d-plot-of-the-error-function-in-a-linear-regression
http://seaborn.pydata.org/generated/sea…