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00000cam a2200000 i 4500 |
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on1391443032 |
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OCoLC |
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20241006213017.0 |
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m o d |
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cr cnu---unuuu |
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230729s2023 sz a ob 001 0 eng d |
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|a EBLCP
|b eng
|e rda
|c EBLCP
|d YDX
|d GW5XE
|d EBLCP
|d OCLCQ
|d OCLCO
|d OCLCF
|d YDX
|d OCLCQ
|d UKAHL
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| 019 |
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|a 1391434017
|a 1396071244
|a 1402038603
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| 020 |
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|a 9783031328794
|q electronic book
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|a 3031328795
|q electronic book
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|z 9783031328787
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|z 3031328787
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| 024 |
7 |
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|a 10.1007/978-3-031-32879-4
|2 doi
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| 035 |
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|a (OCoLC)1391443032
|z (OCoLC)1391434017
|z (OCoLC)1396071244
|z (OCoLC)1402038603
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| 050 |
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4 |
|a Q325.73
|b .J62 2023
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| 050 |
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4 |
|a TK5101-5105.9
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| 049 |
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|a HCDD
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| 100 |
1 |
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|a Jo, Taeho,
|e author.
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| 245 |
1 |
0 |
|a Deep learning foundations /
|c Taeho Jo.
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| 264 |
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1 |
|a Cham, Switzerland :
|b Springer,
|c [2023]
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| 300 |
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|a 1 online resource (xx, 426 pages) :
|b illustrations
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 504 |
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|a Includes bibliographical references and index.
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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 books 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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| 505 |
0 |
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|a Intro -- Preface -- Part I: Foundation -- Part II: Deep Machine Learning -- Part III: Deep Neural Networks -- Part IV: Textual Deep Learning -- Contents -- Part I Foundation -- 1 Introduction -- 1.1 Definition of Deep Learning -- 1.2 Swallow Learning -- 1.2.1 Supervised Learning -- 1.2.2 Unsupervised Learning -- 1.2.3 Semi-supervised Learning -- 1.2.4 Reinforcement Learning -- 1.3 Deep Supervised Learning -- 1.3.1 Input Encoding -- 1.3.2 Output Encoding -- 1.3.3 Unsupervised Layer -- 1.3.4 Convolution -- 1.4 Advanced Learning Types -- 1.4.1 Ensemble Learning -- 1.4.2 Local Learning
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| 505 |
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|a 1.4.3 Kernel-Based Learning -- 1.4.4 Incremental Learning -- 1.5 Summary and Further Discussions -- References -- 2 Supervised Learning -- 2.1 Introduction -- 2.2 Simple Supervised Learning Algorithms -- 2.2.1 Rule-Based Approach -- 2.2.2 Naive Retrieval -- 2.2.3 Data Similarity -- 2.2.4 One Nearest Neighbor -- 2.3 Neural Networks -- 2.3.1 Artificial Neuron -- 2.3.2 Activation Functions -- 2.3.3 Neural Connection -- 2.3.4 Perceptron -- 2.4 Advanced Supervised Learning Algorithms -- 2.4.1 Naive Bayes -- 2.4.2 Decision Tree -- 2.4.3 Random Forest -- 2.4.4 Support Vector Machine
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|a 2.5 Summary and Further Discussions -- References -- 3 Unsupervised Learning -- 3.1 Introduction -- 3.2 Simple Unsupervised Learning Algorithms -- 3.2.1 AHC Algorithm -- 3.2.2 Divisive Algorithm -- 3.2.3 Online Linear Clustering Algorithm -- 3.2.4 K Means Algorithm -- 3.3 Kohonen Networks -- 3.3.1 Initial Version -- 3.3.2 Learning Vector Quantization -- 3.3.3 Semi-supervised Model -- 3.3.4 Self-Organizing Map -- 3.4 EM Algorithm -- 3.4.1 Cluster Distributions -- 3.4.2 Notations -- 3.4.3 E-Step -- 3.4.4 M-Step -- 3.5 Summary and Further Discussions -- Reference -- 4 Ensemble Learning
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| 505 |
8 |
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|a 4.1 Introduction -- 4.2 Partition -- 4.2.1 Training Set -- 4.2.2 Attribute Set -- 4.2.3 Array Partition -- 4.2.4 Partition Schemes -- 4.3 Supervised Combination Schemes -- 4.3.1 Voting -- 4.3.2 Expert Gate -- 4.3.3 Cascading -- 4.3.4 Cellular Learning -- 4.4 Multiple Viewed Learning -- 4.4.1 Views -- 4.4.2 Multiple Encodings -- 4.4.3 Multiple Viewed Supervised Learning -- 4.4.4 Multiple Viewed Unsupervised Learning -- 4.5 Summary and Further Discussions -- Part II Deep Machine Learning -- 5 Deep KNN Algorithm -- 5.1 Introduction -- 5.2 Swallow Version -- 5.2.1 KNN Algorithm -- 5.2.2 KNN Variants
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| 505 |
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|a 5.2.3 Trainable KNN Algorithm -- 5.2.4 Radius Nearest Neighbor -- 5.3 Basic Deep Versions -- 5.3.1 Feature Reduction -- 5.3.2 Kernel-Based KNN Algorithm -- 5.3.3 Output Decoded KNN -- 5.3.4 Pooled KNN -- 5.4 Advanced Deep Versions -- 5.4.1 Unsupervised Layer -- 5.4.2 Unsupervised KNN -- 5.4.3 Stacked KNN -- 5.4.4 Convolutional KNN Algorithm -- 5.5 Summary and Further Discussions -- Reference -- 6 Deep Probabilistic Learning -- 6.1 Introduction -- 6.2 Swallow Version -- 6.2.1 Normal Distribution -- 6.2.2 Bayes Classifier -- 6.2.3 Naive Bayes -- 6.2.4 Bayesian Networks -- 6.3 Basic Deep Versions
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| 588 |
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|a Description based on online resource; title from digital title page (viewed on October 23, 2023).
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| 650 |
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0 |
|a Deep learning (Machine learning)
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| 650 |
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0 |
|a Computer algorithms.
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| 650 |
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7 |
|a algorithms.
|2 aat
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| 650 |
|
7 |
|a Computer algorithms.
|2 fast
|0 (OCoLC)fst00872010
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| 650 |
|
7 |
|a Deep learning (Machine learning)
|2 fast
|0 (OCoLC)fst02032663
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| 776 |
0 |
8 |
|i Print version:
|a Jo, Taeho
|t Deep Learning Foundations
|d Cham : Springer International Publishing AG,c2023
|z 9783031328787
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| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-32879-4
|y Click for online access
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| 903 |
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|a SPRING-ALL2023
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| 994 |
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|a 92
|b HCD
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