Examples of using Training data in English and their translations into Hebrew
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Colloquial
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Ecclesiastic
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Computer
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Programming
Polar OH1 has an internal memory that can store up to 200 hours of training data.
for more on the dangers of predictive models built with biased training data.
As Service is part of the Polar Flow ecosystem, all individual training data is recorded and directly synchronized to Polar Flow service on the member's personal Polar Flow account,
While the green line best follows the training data, it is too dependent on it and it is likely to have a higher error rate on new unseen data, compared to the black line.
connected to J-Light's trial, your idea of creating a semantic network of terms used in the training data set and matching them with their e-mail uses was brilliant.
Over the course of training, common patterns in the training data become reflected in the strengths of the connections,
While the green line best follows the training data, it is too dependent on that data and it is likely to have a higher error rate on new unseen data, compared to the black line._.
will automatically upload to Polar Flow, where all their training data is conveniently available on one platform.
For example, if the task were determining whether an image contained a certain object, the training data for a supervised learning algorithm would include images with
Over the course of training, common patterns in the training data become reflected in the strengths of the connections,
Share all your training data with your coach, get instant feedback
less accidental correlations in the training data tell the network to do different things,
on your wrist or, using the universal bike mount, attach it to your bike's handlebars to easily view your training data while riding.
known as“training data“, in order to make predictions
The service records and shows real-time training data such as heart rate
better algorithms, my insight was to give the algorithms the kind of training data that a child was given through experiences in both quantity and quality.
an overfitted model and the black line represents a regularized model. While the green line best follows the training data, it is too dependent on that data and it is likely to have a higher error rate on new unseen data,
For example, if the task were determining whether an image contained a certain object, the training data for a supervised learning algorithm would include images with
Keep your training data safe when switching devices.
And where do you get your training data from?