We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Why Transformers Work

Formal Metadata

Title
Why Transformers Work
Subtitle
And RNNs Fall Short
Title of Series
Number of Parts
130
Author
License
CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
Identifiers
Publisher
Release Date
Language

Content Metadata

Subject Area
Genre
Abstract
This will be a technical talk where I'll explain the inner workings of the machine learning algorithms inside of Rasa. In particular I'll talk about why the transformer has become a part in many of our algorithms and has replaced RNNs. These include use-cases in natural language processing but also in dialogue handling. You'll see a live demo of a typical error that an LSTM would make but a transformer wouldn't. The algorithms are explained with calm diagrams and very little maths.