Exact(4)
We treat SBIR as a cross-domain modelling problem, in which a depiction invariant embedding of sketch and photo data is learned by regression over a siamese CNN architecture with half-shared weights and modified triplet loss function.
The data is learned by the TensorFlow engine and the final indoor temperature is found.
This kind of data is learned with an evolutionary approach [106].
This data is learned by the TensorFlow engine [40] using the Jupyter editor [41] with the Anaconda package.
Similar(56)
The air conditioner transmits the current indoor temperature to the server and the user's usage patterns and data are learned simultaneously in IE2S to select a suitable temperature level.
This is important because, as those interested in location data are learning, the source of the data is important when considering the quality and accuracy and the types of conclusions you can draw from it.
Once the BBN is learned from data, it can be further used for prediction.
Besides, each of the rules is learned from data without human intervention.
The red part is input data, the green part is learning data, and the part where the two data overlap is represented by yellow.
As one of the visualization techniques proposed by Zeiler and Fergus [15], deconvolution enables us to observe which features of data that are learned by a trained model.
Once the hierarchical data abstractions are learnt from unsupervised data with Deep Learning, more conventional discriminative models can be trained with the aid of relatively fewer supervised/labeled data points, where the labeled data is typically obtained through human/expert input.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com