charade | A server for multilanguage , composable NLP API | Natural Language Processing library

The problem was that a network reasonably functioning in the training mode failed to work in the inference mode. What might be the cause? There are basically two layer types working differently in the two modes: dropout and batch normalization. In MobileNet V. 2, we have only batch normalization, so let's consider how it works.

In the training mode a BN layer calculates batch mean and variance and normalizes the data using these batch values. At the same time it remembers the mean and variance as a moving average weighted with a coefficient called momentum.

Here's an example for the text you used as an example. As discussed in the comments it is not a process that just "works with regex" rather you need to consider a multi-step process. Extract the words you need and then extract the text.

In this case the regex becomes quite hard to read (for my eyes at least) so I did it one step at a time, detailed in the comments.

I go with base-R here, because I want to use the result in a multi-step process but you could have used the interface provided by stringr as well.

The issue is that you are using euclidean distance for comparing strings. Consider using Levenshtein distance, or something similar, which is designed for strings. NLTK has a function called edit distance that can do this or you can implement it on your own.

When inserting data into a MySQL table, you will have to specify which data goes into which column. You do this by specifying the column names before the VALUES part like:

Assuming your dictionary values are simply strings then I would use a replace.

For Example

I had this issue as well. The issue for me was that the html of the oEmbed needs to be wrapped in an and tag with a tag.

Here is an example of this: