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03951nam a2200253 a 4500 |
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201030s2020 enk b 00010 eng |
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|a 9783030497231
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| 035 |
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|l 52517
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| 040 |
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|a GR-PeUP
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| 082 |
0 |
0 |
|a 006.31 MAC
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| 245 |
0 |
0 |
|a Machine learning paradigms :
|b advances in deep learning-based technological applications /
|c George A. Tsihrintzis, Lakhmi C. Jain, editors.
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| 260 |
0 |
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|a Cham :
|b Springer,
|c 2020.
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| 300 |
|
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|a 430 σ. ;
|c 23 εκ.
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| 490 |
1 |
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|a Learning and analytics in intelligent systems ;
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| 500 |
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|a Contents: Intro -- Foreword -- Preface -- Contents -- 1 Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications -- 1.1 Editorial Note -- References -- Part IDeep Learning in Sensing -- 2 Vision to Language: Methods, Metrics and Datasets -- 2.1 Introduction -- 2.2 Challenges in Image Captioning -- 2.2.1 Understanding and Predicting `Importance' in Images -- 2.2.2 Visual Correctness of Words -- 2.2.3 Automatic Evaluation Metrics -- 2.2.4 Image Specificity -- 2.2.5 Natural-Sounding Descriptions -- 2.3 Image Captioning Models and Their Taxonomy Contents: 2.3.1 Example Lookup-Based Models -- 2.3.2 Generation-based Models -- 2.4 Assessment of Image Captioning Models -- 2.4.1 Human Evaluation -- 2.4.2 Automatic Evaluation Metrics -- 2.4.3 Distraction Task(s) Based Methods -- 2.5 Datasets for Image Captioning -- 2.5.1 Generic Captioning Datasets -- 2.5.2 Stylised Captioning Datasets -- 2.5.3 Domain Specific Captioning Datasets -- 2.6 Applications of Visual Captioning -- 2.6.1 Medical Image Captioning -- 2.6.2 Life-Logging -- 2.6.3 Commentary for Sports' Videos -- 2.6.4 Captioning for Newspapers -- 2.6.5 Captioning for Assistive Technology Contents: 2.6.6 Other Applications -- 2.7 Extensions of Image Captioning to Other Vision-to-Language Tasks -- 2.7.1 Visual Question Answering -- 2.7.2 Visual Storytelling -- 2.7.3 Video Captioning -- 2.7.4 Visual Dialogue -- 2.7.5 Visual Grounding -- 2.8 Conclusion and Future Works -- References -- 3 Deep Learning Techniques for Geospatial Data Analysis -- 3.1 Introduction -- 3.2 Deep Learning: A Brief Overview -- 3.2.1 Deep Learning Architectures -- 3.2.2 Deep Neural Networks -- 3.2.3 Convolutional Neural Network (CNN) -- 3.2.4 Recurrent Neural Networks (RNN) -- 3.2.5 Auto-Encoders (AE) Contents: 3.3 Geospatial Analysis: A Data Science Perspective -- 3.3.1 Enabling Technologies for Geospatial Data Collection -- 3.3.2 Geospatial Data Models -- 3.3.3 Geospatial Data Management -- 3.4 Deep Learning for Remotely Sensed Data Analytics -- 3.4.1 Data Pre-processing -- 3.4.2 Feature Engineering -- 3.4.3 Geospatial Object Detection -- 3.4.4 Classification Tasks in Geospatial Analysis -- 3.5 Deep Learning for GPS Data Analytics -- 3.6 Deep Learning for RFID Data Analytics -- 3.7 Conclusion -- References -- 4 Deep Learning Approaches in Food Recognition -- 4.1 Introduction -- 4.2 Background Contents: 4.2.1 Popular Deep Learning Frameworks -- 4.3 Deep Learning Methods for Food Recognition -- 4.3.1 Food Image Datasets -- 4.3.2 Approach #1: New Architecture Development -- 4.3.3 Approach #2: Transfer Learning and Fine-Tuning -- 4.3.4 Approach #3: Deep Learning Platforms -- 4.4 Comparative Study -- 4.4.1 New Architecture Against Pre-trained Models -- 4.4.2 Deep Learning Platforms Against Each Other -- 4.5 Conclusions -- References -- Part IIDeep Learning in Social Media and IOT -- 5 Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding -- 5.1 Introduction.
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| 504 |
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|a Περιλαμβάνει βιβλιογραφία.
|
| 650 |
|
4 |
|a Machine learning.
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| 650 |
|
4 |
|a Μάθηση.
|
| 700 |
1 |
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|a Τσιχριντζής, Γεώργιος Α.
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| 700 |
1 |
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|a Jain, L. C.
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| 830 |
|
2 |
|a Learning and analytics in intelligent systems ;
|v 18
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| 852 |
|
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|a INST
|b UNIPILB
|c MAIN
|e 20201031
|h 006.31 MAC
|p 00183756
|q 00183756
|t LOAN
|y 0
|4 1
|
| 856 |
4 |
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|d /webopac/covers/03/52517_9783030497231.jpg
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