Deep Learning for information extraction: Improve your extraction results
This is the second part of a series of articles about Deep Learning methods for Natural Language Processing applications. As mentioned in the previous blog post, we will now go deeper into different strategies of extending the architecture of our system in order to improve our extraction results. This post will elaborate on techniques like word embeddings, residual connections, conditional random fields, as well as character embeddings.
How you create rapid machine learning prototypes (with sample use cases)
Businesses, decision makers and machine learning practitioners share a common struggle: How do we identify business problems worth turning into data problems, so we can create data-empowered solutions that result in business value? Read further to get an answer to that question!