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Rasa Algorithm Whiteboard - Countvectors and Spelling Errors
Rasa Algorithm Whiteboard - Subword Embeddings and Spelling
Rasa Algorithm Whiteboard - Sparsity
Introducing The Algorithm Whiteboard
Rasa Algorithm Whiteboard - Measuring Quality with Markers
Rasa Algorithm Whiteboard - Model Confidence v2
Rasa Algorithm Whiteboard - NER for Personal Indentifiable Information is Hard
Rasa Algorithm Whiteboard - Detecting Names
Rasa Algorithm Whiteboard - TED Policy
Rasa Algorithm Whiteboard - Using Projections to Remove Bias from Word Embeddings
Rasa Algorithm Whiteboard - Translation Issues
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Last Updated: October 18, 2026
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Summary
This is the first video on attention mechanisms. We'll start with self attention and end with transformers. We're going at it step by ... In this video we'll highlight the main points of the starspace paper. It's an influencial piece of work and here at In this video, we'll highlight a qualitative argument of why you may not need to worry about pre-trained embeddings too much. Countvectors are still an unsung hero in natural language processing. This is especially true in It turns out that you can drop a lot of weights from your neural network without seeing a drastic effect on accuracy. In this video ... We're starting a new playlist! Introducing the How do you judge the quality of a virtual assistant? Can we look at the intent classification scores and call it a day? Or do we need ... This video is the second version of an original video. The content of the original video still holds true, but we decided to change ... Detecting personally identifiable information from text is typically a NER problem. It's an important problem, especially when ... In this video, we will explore pre-trained spaCy models and use them to detect names. These pre-trained models are great, but ... When you're making a digital assistant you'll need more than just In the previous video, we've started measuring bias in word embeddings. In this video, we will attempt to remove some of this bias ... Text translation is hard, especially when you've only gotten a small amount of data to work with. But still ... we may be interested in ...
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