Examples of using We trained in English and their translations into Chinese
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Political
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Ecclesiastic
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Programming
For example, we trained a large language model on 15x more data, which generated a language model containing 19.2 billion N-grams within a few hours.
We trained all of the time; eventually we went to dog shows and I showed her in the obedience ring.
I miss driving from my home in Palm Beach Gardens to the Chris Evert Academy in Boca Raton in Florida, where we trained.
I feel like people watching this game will not believe how hard we trained this week.
We trained a convolutional neural network(CNN) to predict the probability that a given Kepler signal is caused by a planet.
Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD.
We trained people in the church in how to use it, and we used it out on the field.
We trained a generative neural network to invent new fragments, which were used to continually improve the score of the proposed protein structure.
We trained a neural network to predict a separate distribution of distances between every pair of residues in a protein.
We trained the last four years to participate in this marathon,” she said.
We trained a neural network to predict a separate distribution of distances between every pair of residues in a protein.
We trained a generative neural network to invent new fragments, which were used to continually improve the score of the proposed protein structure.
We trained a neural net to eliminate all the bugs in the app and it deleted everything.
We trained a neural net to eliminate all the bugs in the app and it deleted everything.
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes.
To deliver quality vision care to rural students, we trained teachers to conduct vision screenings in their schools.
We trained our whole lives on standards and ethics,” she added.
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes.
We trained a computer model to find things in imagery that are predictive of poverty," said Dr Burke.