Sentence examples for more deep learning from inspiring English sources
Chances are, engineers will use more deep learning and machine learning technology to better understand how people use language to perform searches.
This practice may require students to use more deep learning.
The affective dimension is stated to manifest itself in two ways: increased 'time on task' and/or a shift in learning style from superficial to more deep learning.
School performance has been shown to correlate poorly with students' performance in the university [ 5], possibly because university education requires more deep learning and analytical thinking compared to simple factual recall required for advanced level or equivalent school examinations.
It has been found that intrinsic motivation, as compared to controlled motivation, leads to greater creativity [ 13], less superficial information processing [ 14], more deep learning [ 15, 16], higher achievement [ 17, 18], enhanced well-being or adjustment [ 19, 20], decreased drop-out intention and behaviour [ 21, 22].
One immediate question we're asking is, how does the work from Hacktivation Nation show up as more deeper learning in schools?
Getting acquired has offered Movidius some more flexibility to add features like the ability to plug in several of these sticks to add more deep-learning power.
Students also tend to expect more higher-level questions from CR exams and employ more deep-learning strategies in preparation for them (Scouller, 1998).
Therefore, more effective deep learning hashing for particular mobile visual search applications with multi-modality fusion will be more attractive in the future.
And I'm interested in understanding more because there are many remaining questions, and answers to these questions will not only tell us more about Deep Learning, but may help us understand Learning, Inference, and Representation in general.
More recently, deep learning researchers have emulated neural architectures to perform automated image segmentation and textural feature-based classification following training on large datasets of medical images.
