Deep Learning Foundations
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 ensembl...
Zapisane w:
| 1. autor: | |
|---|---|
| Korporacja: | |
| Format: | Elektroniczne E-book |
| Język: | angielski |
| Wydane: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
|
| Wydanie: | 1st ed. 2023. |
| Hasła przedmiotowe: | |
| Dostęp online: | https://doi.org/10.1007/978-3-031-32879-4 |
| Etykiety: |
Dodaj etykietę
Nie ma etykietki, Dołącz pierwszą etykiete!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-32879-4 | ||
| 003 | DE-He213 | ||
| 005 | 20260810170347.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 230725s2023 sz | o |||| 0|eng d | ||
| 020 | |a 9783031328794 |9 978-3-031-32879-4 | ||
| 024 | 7 | |a 10.1007/978-3-031-32879-4 |2 doi | |
| 050 | 4 | |a TK5101-5105.9 | |
| 072 | 7 | |a TJK |2 bicssc | |
| 072 | 7 | |a TEC041000 |2 bisacsh | |
| 072 | 7 | |a TJK |2 thema | |
| 082 | 0 | 4 | |a 621.382 |2 23 |
| 100 | 1 | |a Jo, Taeho. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Deep Learning Foundations |h [electronic resource] / |c by Taeho Jo. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (XX, 426 p.). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of color is not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background color |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All non-decorative content supports reading without sight |2 onix | |
| 347 | |a text file |b PDF |2 rda | ||
| 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. . | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Telecommunication. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Neural networks (Computer science) . | |
| 650 | 1 | 4 | |a Communications Engineering, Networks. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Mathematical Models of Cognitive Processes and Neural Networks. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031328787 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031328800 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031328817 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-32879-4 |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||