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About 10,000 papers for “deep learning”

Learning Deep Learning

Arruda,Henrique F. de, Benatti,Alexandre, Comin,César Henrique et al. · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2022

As a consequence of its capability of creating high level abstractions from data, deep learning has been effectively employed in a wide range of applications, including physics. Though deep learning can be, at first and simplistically understood in terms of very large neural networks, it also encompasses new concepts and methods. In order to understand and apply deep learning, it is important to become familiarized with the respective basic concepts. In this text, after briefly revising some works relating to physics and deep learning, we introduce and discuss some of the main principles of deep learning as well as some of its principal models. More specifically, we describe the main elements, their use, as well as several of the possible network architectures. A companion tutorial in Python has been prepared in order to complement our approach.

Cited by 0Open access

Deep Learning Deep dive

Vallecorsa, Sofia · CERN Document Server (European Organization for Nuclear Research) · 2021

Cited by 0

therealzaini/basic-deep-learning: basic-deep-learning

therealzaini · Zenodo (CERN European Organization for Nuclear Research) · 2025

No description provided.

Cited by 0Open access

Deep Learning

Kyunghyun Cho · 2018

Deep learning has rapidly gained huge popularity among researchers in natural-language processing and computational linguistics in recent years. This chapter gives a comprehensive and detailed overview of recent deep-learning-based approaches to challenging problems in natural-language processing, specifically focusing on document classification, language modelling, and machine translation. At the end of the chapter, new opportunities in natural-language processing made possible by deep learning are discussed, which are multilingual and larger-context modelling.

Cited by 1

Deep Learning

Machine Learning · 2021

Cited by 4

Deep Learning

Kwangjo Kim, Muhamad Erza Aminanto, Harry Chandra Tanuwidjaja · SpringerBriefs on cyber security systems and networks · 2018

Cited by 2

Deep Learning

Amanda Naylor, Janet Gibbs · International Journal of Mobile and Blended Learning · 2017

This article presents results from an international collaboration between college students and pre-service teachers in Norway and the UK. This research is part of a large, international project exploring and developing the interrelationship between mobile technology and teachers' perceptions of teaching and learning. Data was collected for this study through an on-line survey of 37 pre-service teachers followed by six semi-structured, in-depth interviews. The data analysis revealed the themes of collaboration, authenticity and professional learning through the use of mobile technology in the data. The collaboration enabled the use of the affordances of mobile technology to enhance the pre-service teachers' professional learning and the data suggested that this enhanced their emergent conceptions of teaching and learning.

Cited by 24Open access

Deep Learning

Nikita Patil, Krishna Kadam, Rahul Patil · IJARCCE · 2018

Deep learning is used to identify the objects with the help of neural network. It is used to identify various objects in the world. In this, we have explained the process of identifying the objects with the help of various layers. Machine uses different layers to identify the object with the help of hidden layers which is used to train the machine depending upon the database provided to it. The more the database provided to it the more the accurate result the machine will provide.

Cited by 2Open access

Deep Learning

Sunil Kumar · 2024

Cited by 0

Deep Learning

Taşkın Kavzoğlu, Brandt Tso, Paul M. Mather · 2024

Cited by 0

Deep Learning

Chirag Shah · Cambridge University Press eBooks · 2022

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.

Cited by 1

Deep Learning

Hui Lin, Ming Li · 2023

Cited by 2

Deep Learning

RaviKumar Bhuvanagiri · SSRN Electronic Journal · 2026

Cited by 0Open access

Deep learning

Roli, Andrea · AMS Campus - AlmaDL · 2017

Cited by 0

Deep Learning

Manish Gupta · RePEc: Research Papers in Economics

Abstract Deep learning has caught a great deal of momentum in the last few years. Research in the field of deep learning is progressing very fast. Deep learning is a rapidly growing area of machine learning. Machine learning (ML) has seen numerous successes, but applying traditional ML algorithms today often means spending a long time hand-engineering the domain-specific input feature representation. This is true for many problems in vision, audio, natural language processing (NLP), robotics, and other areas. To address this, researchers have developed deep learning algorithms that automatically learn a good high-level abstract representation for the input. These algorithms are today enabling many groups to achieve groundbreaking results in vision recognition, speech recognition, language processing, robotics, and other areas.

Cited by 0