Context Briefing: In this video, we'll highlight a qualitative argument of why you may not need to worry about pre-trained embeddings too much. What if we design a policy mechanism that doesn't predict the next action to take, but instead predicts when something ...
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Information Main Overview
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'll highlight a qualitative argument of why you may not need to worry about pre-trained embeddings too much. What if we design a policy mechanism that doesn't predict the next action to take, but instead predicts when something ...
Information Important Notes
What if we design a policy mechanism that doesn't predict the next action to take, but instead predicts when something ... It's easy to get distracted when you go down the rabbit hole of performance statistics.
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In the previous video we introduced keys, queries and values and in this video ... Sometimes you can change the intent labels to get a more accurate model. When you iterate on your data, you also want to iterate on your model.
Verification Tips
When you iterate on your data, you also want to iterate on your model. We'll be exploring a trick that sounds like it might work, but we can only confirm it by receiving ...
Relevant points collected here
- What if we design a policy mechanism that doesn't predict the next action to take, but instead predicts when something ...
- In this video, we'll highlight a qualitative argument of why you may not need to worry about pre-trained embeddings too much.
- It turns out that you can drop a lot of weights from your neural network without seeing a drastic effect on accuracy.
- We'll be exploring a trick that sounds like it might work, but we can only confirm it by receiving ...
- It's easy to get distracted when you go down the rabbit hole of performance statistics.
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