The phrase "deep learning methodologies" is grammatically correct and can be used in written English.
It refers to the various approaches, techniques, and strategies used in the field of deep learning, which is a subset of artificial intelligence focused on training computer systems to learn and make decisions on their own. An example of using this phrase in a sentence could be: "The success of our project was largely attributed to the effective implementation of various deep learning methodologies, such as convolutional neural networks and recurrent neural networks."
There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased.
In this paper, convolutional neural network models were developed to perform plant disease detection and diagnosis using simple leaves images of healthy and diseased plants, through deep learning methodologies.
One camp of companies believes that decision making can be handled best with deep learning methodologies.
The technology leverages deep learning methodologies and a huge data repository to map images and videos of faces to a read the emotions expressed in the face.
This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field.
To our best knowledge, this is the first systematic model attempting to introduce the deep learning methodology to the ESN modeling, which provides a more robust alternative to the conventional shallow ESNs.
In some ways, the deep learning methodology makes things simpler for SEOs.
In September, Google showed how its approach, using the currently popular deep learning methodology, could not just recognize images of single objects but also classify different objects in a single image (think different kinds of fruits in a fruit basket, for example).
Here we argue that now is the time to create smarter healthcare systems in which the best treatment decisions are computationally learned from electronic health record data by deep-learning methodologies.
Above other machine learning methodologies, deep learning has become particularly attractive for mining patterns from large data sets.
They were organized in topical sections named: network analysis and modelling; genomic data analysis; cancer data analysis; structure and interaction; HPC and CryoEM; machine and deep learning; data analysis and methodology; analysis and visualization tools; and RNA-Seq data analysis.
