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Deep learning foundations

Jo, Taeho.

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Deep learning foundations

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning foundations/ by Taeho Jo.
作者:	Jo, Taeho.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xx, 426 p. :ill., digital ;24 cm.
內容註:	Introduction -- Part I. Foundation -- Supervised Learning -- Unsupervised Learning -- Ensemble Learning -- Part II. Deep Machine Learning -- Deep K Nearest Neighbor -- Deep Probabilistic Learning -- Deep Decision Tree -- Deep SVM -- Part III. Deep Neural Networks -- Multiple Layer Perceptron -- Recurrent Networks -- Restricted Boltzmann Machine -- Convolutionary Neural Networks -- Part IV. Textual Deep Learning -- Index Expansion -- Text Summarization -- Textual Deep Operations -- Convolutionary Text Classifier -- Conclusion.
Contained By:	Springer Nature eBook
標題:	Deep learning (Machine learning) -
電子資源:	https://doi.org/10.1007/978-3-031-32879-4
ISBN:	9783031328794

Deep learning foundations
Jo, Taeho.

Deep learning foundations[electronic resource] /by Taeho Jo. - Cham :Springer International Publishing :2023. - xx, 426 p. :ill., digital ;24 cm.

Introduction -- Part I. Foundation -- Supervised Learning -- Unsupervised Learning -- Ensemble Learning -- Part II. Deep Machine Learning -- Deep K Nearest Neighbor -- Deep Probabilistic Learning -- Deep Decision Tree -- Deep SVM -- Part III. Deep Neural Networks -- Multiple Layer Perceptron -- Recurrent Networks -- Restricted Boltzmann Machine -- Convolutionary Neural Networks -- Part IV. Textual Deep Learning -- Index Expansion -- Text Summarization -- Textual Deep Operations -- Convolutionary Text Classifier -- Conclusion.

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book's third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning. Provides a conceptual understanding of deep learning algorithms; Presents ways of modifying existing machine learning algorithms into deep learning algorithms for further analysis; Details how deep learning can solve problems such as classification, regression, and clustering.

ISBN: 9783031328794

Standard No.: 10.1007/978-3-031-32879-4doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Deep learning foundations
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505 0 $a Introduction -- Part I. Foundation -- Supervised Learning -- Unsupervised Learning -- Ensemble Learning -- Part II. Deep Machine Learning -- Deep K Nearest Neighbor -- Deep Probabilistic Learning -- Deep Decision Tree -- Deep SVM -- Part III. Deep Neural Networks -- Multiple Layer Perceptron -- Recurrent Networks -- Restricted Boltzmann Machine -- Convolutionary Neural Networks -- Part IV. Textual Deep Learning -- Index Expansion -- Text Summarization -- Textual Deep Operations -- Convolutionary Text Classifier -- Conclusion.
520 $a This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book's third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning. Provides a conceptual understanding of deep learning algorithms; Presents ways of modifying existing machine learning algorithms into deep learning algorithms for further analysis; Details how deep learning can solve problems such as classification, regression, and clustering.
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