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)))

What is the desired addition or change?

It would be nice to have some time series methods. Like

1. naive
2. seasonal naive
3. seasonal trend loess decomposition.
4. Holt Winters
5. Exponential smoothing
6. Arima

Since many time series come in groups it may be useful to think about how the data should be organized to take in many time series and account for seasonality. Also prob

Unlike N-Gram, the current Skip-Gram tokenizer does not allow variable length tokens. This ticket is to implement a min max scheme similar to the N-Gram tokenizer such that each token is a k-skip-n-gram. In other words, instead of fi

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