Deep Learning
Deep Learning
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DOI：
10.4018/978-1-5225-7862-8.ch007
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
K. Lakhtaria;Darshankumar Modi
中科院分区：
文献类型：
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作者：
K. Lakhtaria;Darshankumar Modi
深度学习是机器学习的一个子集。顾名思义，深度学习意味着越来越多的层。深度学习基本上是基于神经元的原理。随着大数据或大量数据的增加，深度学习方法和技术被广泛用于提取有用的信息。深度学习可以应用于计算机视觉、生物信息学、语音识别或自然语言处理。本章涵盖了深度学习的基础知识，不同的深度学习架构，如人工神经网络，前馈神经网络，CNN，递归神经网络，深度玻尔兹曼机，以及它们的比较。本章还总结了深度学习在不同领域的应用。
Deep learning is a subset of machine learning. As the name suggests, deep learning means more and more layers. Deep leaning basically works on the principle of neurons. With the increase in big data or large quantities of data, deep learning methods and techniques have been widely used to extract the useful information. Deep learning can be applied to computer vision, bioinformatics, and speech recognition or on natural language processing. This chapter covers the basics of deep learning, different architectures of deep learning like artificial neural network, feed forward neural network, CNN, recurrent neural network, deep Boltzmann machine, and their comparison. This chapter also summarizes the applications of deep learning in different areas.