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02857nmm a2200361 u 4500 |
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EBX01000000000000001308060 |
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00000000000000.0 |
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230808 ||| eng |
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|a 9783031328794
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| 100 |
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|a Jo, Taeho
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| 245 |
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|a Deep Learning Foundations
|h Elektronische Ressource
|c by Taeho Jo
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| 250 |
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|a 1st ed. 2023
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2023, 2023
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| 300 |
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|a XX, 426 p
|b online resource
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| 505 |
0 |
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|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
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| 653 |
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|a Machine learning
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| 653 |
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|a Machine Learning
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Mathematical Models of Cognitive Processes and Neural Networks
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Neural networks (Computer science)
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| 653 |
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|a Telecommunication
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| 653 |
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|a Communications Engineering, Networks
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| 653 |
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|a Automated Pattern Recognition
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| 653 |
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|a Pattern recognition systems
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
5 |
0 |
|a 10.1007/978-3-031-32879-4
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-031-32879-4?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 621,382
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| 520 |
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|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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