Learn more about computerscience with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
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.
4
61 reads
MORE IDEAS ON THIS
The most common avenues for collecting training data:
A combination of all four will provide the best training data.
4
59 reads
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...
4
46 reads
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 ...
5
63 reads
CURATED FROM
IDEAS CURATED BY
Read & Learn
20x Faster
without
deepstash
with
deepstash
with
deepstash
Personalized microlearning
—
100+ Learning Journeys
—
Access to 200,000+ ideas
—
Access to the mobile app
—
Unlimited idea saving
—
—
Unlimited history
—
—
Unlimited listening to ideas
—
—
Downloading & offline access
—
—
Supercharge your mind with one idea per day
Enter your email and spend 1 minute every day to learn something new.
I agree to receive email updates