Note: This is not one convertor for all frameworks, but a collection of different converters. Because github is an open source platform, I hope we can help each other here, gather everyone's strength.

Deep learning models owe their initial success to large servers with large amounts of memory and clust This article is part of our reviews of AI research papers, a series of posts that explore the ...

"The processing of the signals within the individual cells follows different mathematical principles than previous deep learning models," says Dr. Ramin Hasani, postdoctoral associate at the ...

End-to-End Learning: In deep learning, models are typically trained in an end-to-end manner, meaning they take raw data as input and produce predictions without requiring manual feature engineering.

In general, classical (non-deep) machine learning algorithms train and predict much faster than deep learning algorithms; one or more CPUs will often be sufficient to train a classical model.

Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. Because it is the most general way to model a problem, deep learning has the ...

Decision making algorithms are used in a multitude of different applications. Conventional approaches for designing decision algorithms employ principled and simplified modelling, based on which one ...

For model training, the authors discussed how to combine deep active learning with current data-heavy mainstream methods, including supervised training, semi-supervised learning, transfer learning ...