Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))

I tried some RNN regression learning based on the code in the "PyTorch-Tutorial/tutorial-contents/403_RNN_regressor.py" file, which did not work for me at all.

According to an accepted answer on stack-overflow (https://stackoverflow.com/questions/52857213/recurrent-network-rnn-wont-learn-a-very-simple-function-plots-shown-in-the-q?noredirect=1#comment92916825_52857213), it turns out that the li

This is a good first issue and will help new contributors to get familiar with the codebase. Also This issue doesn't aim to add all Metrics to mlpack since each metric would have to be maintained, this aims to add metrics that either I find essential (or have used a couple of time) or those metrics which are very common.
List of metrics that can be added include:

1. IoU and meanIoU

From the docs for https://simplestatistics.org/docs/#standardnormaltable:

The table used is the cumulative, and not cumulative from 0 to mean.

I'm having trouble understanding this sentence. What is meant by cumulative and not cumulative?

The help currently reads:
Vector S get; set;
Gets the singular values (Σ) of matrix in ascending value.

It should be:
descending order of magnitude.

1. 31页，第一行
   
   应改成
   

2. 35 页， 正定矩阵的定义 （公式 2-15）
   标准正定矩阵的定义，应该没有 “对称矩阵” 这一条件吧？ 只是您在本书大多讨论的是“对称”正定矩阵，所以加上了 “对称” 这一条件？

3. 38页， 奇异值分解定义（公式 2-16）
   ![default](https://

From alan-turing-institute/MLJBase.jl#68:

This doesn't work:

@mlj_model mutable struct Bar
    a::Int = -1::(_ > -2)
end

But this does:

@mlj_model mutable struct Bar
    a::Int = (-)(1)::(_ > -2)
end

This needs to be documented in MLJ/docs/src/adding_models_for_general_use and MLJ/docs/src/quick_start_guide_to_adding_models

Hi @JavierAntoran @stratisMarkou,

First of all, thanks for making all of this code available - it's been great to look through!

Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac

The docstrings for e.g. ftest are not present in the generated
documentation but are quite useful. I think the full API should be in
the generated docs.

To be verbose on what is actually done.

It would also be nice to have an online resource that one could link to which describes its behavior.
This would optimally include examples that skip certain settings in case of learner issues with such.

Can we export the autotest and tag it as "internal" so that we get a rendered version of a (not yet existing) documentation?