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Naveed [7] used a learning approach based on pure content features to predict the probability of a message being retweeted.
For VGG-16 and ResNet-50, we used a learning rate of 0.001 for the spatial streams.
We used a learning collaborative quality improvement (QI) model to assess the effect of a multicomponent intervention on reducing MOs.
The key inclusion criteria were that the study came from K-12 or higher education settings, used experimental or quasi-experimental research designs, and used a learning outcome measure to evaluate the effects of the virtual reality-based instruction.
First, our cerebellar model used a learning rule that simulated supervised learning that takes place in the cerebellum and driven by the climbing fibers messages issued from the inferior olive.
The course design used a learning theory approach, was based on participants' needs-assessments, had an interactive format, used case-based learning with performance feedback, and was tailored to local STD problems and included reinforcement components (e.g., mail consultation, and learning materials).
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In addition, searching and using a learning object in learning object repository (LOR) may take too much time.
The even-numbered groups are later used for evaluation; therefore, they are processed using a learning constant of ε = 0.0.
We investigated this question using a learning transfer paradigm.
Training a simple classifier uses a learning rate of 0.001, reducing by 10% every 5000 iterations.
Additionally, the adaptation step uses a learning rule to modify and update the weights.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com