Sentence examples for deep learning architectures from inspiring English sources

Different deep learning architectures, such as CNNs and RNNs, support different types of applications (image, text, etc.).

We show that deep learning architectures can capture these nonlinear spatio-temporal effects.

In this paper, we discuss some widely-used deep learning architectures and their practical applications.

We then describe two main components of deep learning, i.e., deep learning architectures and model optimization.

The majority of deep learning architectures described in the literature primarily focus on extracting spatial features.

Stochastic Gradient Descent is the basic optimization algorithm behind powerful deep learning architectures which are becoming increasingly omnipresent in society.

Schnet a deep learning architecture for molecules and materials.

An empirical comparison of the deep learning architecture is done.

Second, a novel deep accumulated attributes learning architecture is proposed based on very deep learning architecture VGG16.

This associative memorization architecture is constructed by using deep learning architecture.

The model has been designed using deep learning architecture with handcrafted features.