Topic Brief: How to squeeze more bits of those physically available from an Analog to Digital Converter? In this video, we cover how to handle imbalanced data in classification-type machine learning problems.
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Topic Useful Overview
In this video, we cover how to handle imbalanced data in classification-type machine learning problems. If you run out of headroom with your chosen sample rate, how do you avoid the problems of unwanted harmonics?
Context Practical Context
Learn the ins and outs of hard clipping versus soft clipping, as well as a simplified How to squeeze more bits of those physically available from an Analog to Digital Converter?
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- If you run out of headroom with your chosen sample rate, how do you avoid the problems of unwanted harmonics?
- In this video, we cover how to handle imbalanced data in classification-type machine learning problems.
- How to squeeze more bits of those physically available from an Analog to Digital Converter?
- Learn the ins and outs of hard clipping versus soft clipping, as well as a simplified
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