Service Oriented Architecture and Distributed Computing using LASAGNE

Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible. Highly effective DL techniques help to find more hidden knowledge. Deep learning has a promising future due to its great performance and accuracy. We need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively. A survey on DL ways, advantages, drawbacks, architectures, and methods to have a straightforward and clear understanding of it from different views is explained in the paper. Moreover, the existing related methods are compared with each other, and the application of DL is described in some applications, such as medical image analysis, handwriting recognition, and so on.

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Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

Gheisari

Ebrahimzadeh

Rahimi

et al. 2023

CAAI Trans on Intel Tech

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Service Oriented Architecture and Distributed Computing using LASAGNE

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Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

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