Deep learning in optics—a tutorial Article Swipe

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Barak Hadad , Sahar Froim , Erez Yosef , Raja Giryes , Alon Bahabad ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1088/2040-8986/ad08dc

In recent years, machine learning and deep neural networks applications have experienced a remarkable surge in the field of physics, with optics being no exception. This tutorial aims to offer a fundamental introduction to the utilization of deep learning in optics, catering specifically to newcomers. Within this tutorial, we cover essential concepts, survey the field, and provide guidelines for the creation and deployment of artificial neural network architectures tailored to optical problems.

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Computer science Artificial neural network Software deployment Deep learning Field (mathematics) Cover (algebra) Artificial intelligence Optics Physics Engineering Software engineering Mechanical engineering Pure mathematics Mathematics

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Type: article
Language: en
Landing Page: https://doi.org/10.1088/2040-8986/ad08dc
OA Status: hybrid
Cited By: 8
References: 295
Related Works: 10
OpenAlex ID: https://openalex.org/W4388210361

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DOI: https://doi.org/10.1088/2040-8986/ad08dc

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Title: Deep learning in optics—a tutorial

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-11-02

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Authors: Barak Hadad, Sahar Froim, Erez Yosef, Raja Giryes, Alon Bahabad

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Landing page: https://doi.org/10.1088/2040-8986/ad08dc

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Concepts: Computer science, Artificial neural network, Software deployment, Deep learning, Field (mathematics), Cover (algebra), Artificial intelligence, Optics, Physics, Engineering, Software engineering, Mechanical engineering, Pure mathematics, Mathematics

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Cited by: 8

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