Titlebook: Advances in Deep Learning; M. Arif Wani,Farooq Ahmad Bhat,Asif Iqbal Khan Book 2020 Springer Nature Singapore Pte Ltd. 2020 Deep Learning.

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Basics of Supervised Deep Learning,ed, and practice-focused chapters from leading international experts. It demonstrates how positive education offers an approach to understanding learning that blends academic study with life skills such as self-awareness, emotion regulation, healthy mindsets, mindfulness, and positive habits, ground

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Supervised Deep Learning in Fingerprint Recognition, Johnson.Explores the importance of personal relations in diThis handbook examines the personal relationships between American presidents and British prime ministers. It aims to determine how personal diplomacy shaped the Anglo-American relationship and whether individual leaders made the relationsh

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Unsupervised Deep Learning in Character Recognition,derpinning alcohol use and misuse, discusses the interventions that can be designed around these theories, and offers key insight into future developments within the field..A range of international experts assess the unique factors that contribute to alcohol-related behaviour as differentiated from

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2197-6503 al advanced concepts like classification, face recognition, .This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neur

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Chromatin Structure in Senescent Cellsearning more and more feasible for various applications. Since the main focus of this chapter is on supervised deep learning, Convolutional Neural Network (CNN or ConvNets) that is one of the most commonly used supervised deep learning models is discussed in this chapter.

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Gowrishankar Banumathy,Peter D. Adamsrs, is done by using a large set of labeled data. Some of the supervised CNN architectures proposed by researchers include LeNet-5, AlexNet, ZFNet, VGGNet, GoogleNet, ResNet, DenseNet, and CapsNet. These architectures are briefly discussed in this chapter.

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