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Convergence of deep learning In cyber iot systems and security

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Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

006.3 RAJ c

Penerbit

USA : Scrivener., 2023

Deskripsi Fisik

472 hlm

Bahasa

English

ISBN/ISSN

9781119857211

Klasifikasi

006.3

Tipe Isi

text

Tipe Media

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Tipe Pembawa

-

Edisi

Ed. 1

Subjek

FT
ebook

Info Detail Spesifik

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Pernyataan Tanggungjawab

Rajdeep Chakraborty

Versi lain/terkait

Lampiran Berkas

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