Learn more about computerscience with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
Collect data from a variety of sources.
The most common avenues for collecting training data:
- Paying for data sets
- Using public data sets
- Sourcing open source content
- Using in-person or field-collected data sets
A combination of all four will provide the best training data.
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Monitor real-world performance
Once you've ensured your initial data set is diverse, your model can still have bias.
That's why you should monitor your model's real-world performance. For example, does your model better predict female speech over male speech? If so, retrain with new datasets to overcome any problem area...
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Ensure diverse data
Ensure the data is diverse. Speakers in the audio or video files should possess a range of characteristics, including locations, dialects, genders, sex, race, and nationality.
Sourcing such data could be difficult if you only rely on open-source data.
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AI models trained with biased data
Data bias can have notable implications for research and practical applications. For example, in a Facebook scandal, its AI shockingly asked users if they wanted to continue seeing videos about primates after watching a video featuring Black men.
Data bias refers to data sets that ...
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The best list of all is your own customer list.
Nothing will add to your bottom line as fast as selling something new to your existing customers and sourcing new prospects.
Characteristics of “Hot” Lists
- Recency (most important): The more recently a person has purchased...
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