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Artificial neural networks and deep learning
American Journal of Orthodontics and Dentofacial OrthopedicsThis chapter provides a comprehensive overview of Artificial Neural Networks (ANNs) and Deep Learning (DL), tracing their historical development, fundamental principles, and advanced architectures. Beginning with the foundational concepts of ANNs, including neuron structures, activation functions, and learning processes, the discussion extends to deep ...
Melvin Geubbelmans +3 more
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Artificial Neural Networks and Deep Learning
, 2019 An introduction on Aritifical Neural Networks and Deep Learning: * Multilayer Perceptrons * Convolutional Neural Networks (CNN)
Vahid Mirjalili (6376526)
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What Is Machine Learning, Artificial Neural Networks and Deep Learning?-Examples of Practical Applications in Medicine. [PDF]
Diagnostics (Basel), 2023 Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years.
Kufel J +13 more
europepmc +2 more sources
Artificial Neural Networks [PDF]
, 2023 This book examines artificial neural networks (ANNs) and their applications in various fields. Chapters address ANNs and deep learning techniques for real-world applications in health care, materials processing, energy management, and ...
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Learning Trajectories of Hamiltonian Systems with Neural Networks [PDF]
, 2022 Modeling of conservative systems with neural networks is an area of active research. A popular approach is to use Hamiltonian neural networks (HNNs) which rely on the assumptions that a conservative system is described with Hamilton\u27s equations of ...
Haitsiukevich, Katsiaryna +1 more
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Artificial Intelligence in Surgery: Neural Networks and Deep Learning
CoRR, 2020 Deep neural networks power most recent successes of artificial intelligence, spanning from self-driving cars to computer aided diagnosis in radiology and pathology. The high-stake data intensive process of surgery could highly benefit from such computational methods.
Deepak Alapatt +3 more
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Learning Deep Belief Networks from Non-Stationary Streams [PDF]
, 2012 18.10.13 KB. Ok to add author version to spiral from LNCS; embargo period expired. SpringerDeep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets.
Pouzols, Federico Montesino +11 more
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Decorrelation-Based Deep Learning for Bias Mitigation
Future Internet, 2022 Although deep learning has proven to be tremendously successful, the main issue is the dependency of its performance on the quality and quantity of training datasets. Since the quality of data can be affected by biases, a novel deep learning method based
Pranita Patil, Kevin Purcell
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Survey of Graph Neural Network [PDF]
Jisuanji gongcheng, 2021 With the continuous development of the computer and Internet technologies,graph neural network has become an important research area in artificial intelligence and big data.Graph neural network can effectively transmit and aggregate information between ...
WANG Jianzong, KONG Lingwei, HUANG Zhangcheng, XIAO Jing
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Frontiers in Computational Neuroscience, 2022 IntroductionIn recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance ...
Cai Wingfield +9 more
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