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1
Hands-On Deep Learning with R: A Practical Guide to Designing, Building, and Improving Neural Network Models Using R
Hands-On Deep Learning with R: A Practical Guide to Designing, Building, and Improving Neural Network Models Using R
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Hands-On Deep Learning with R: A Practical Guide to Designing, Building, and Improving Neural Network Models Using R

ISBN: 9781788996839 ;ISBN: 1788996836 ;EISBN: 1788993780 ;EISBN: 9781788993784 ;OCLC: 1153089714

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2
The Deep Learning Workshop: Learn the Skills You Need to Develop Your Own Next-Generation Deep Learning Models with TensorFlow and Keras
The Deep Learning Workshop: Learn the Skills You Need to Develop Your Own Next-Generation Deep Learning Models with TensorFlow and Keras
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The Deep Learning Workshop: Learn the Skills You Need to Develop Your Own Next-Generation Deep Learning Models with TensorFlow and Keras

EISBN: 1839210567 ;EISBN: 9781839210563 ;OCLC: 1189773751

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3
Hands-On One-shot Learning with Python: Learn to Implement Fast and Accurate Deep Learning Models with Fewer Training Samples Using Pytorch
Hands-On One-shot Learning with Python: Learn to Implement Fast and Accurate Deep Learning Models with Fewer Training Samples Using Pytorch
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Hands-On One-shot Learning with Python: Learn to Implement Fast and Accurate Deep Learning Models with Fewer Training Samples Using Pytorch

ISBN: 9781838825461 ;ISBN: 1838825460 ;EISBN: 1838824871 ;EISBN: 9781838824877 ;OCLC: 1151186067

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4
Hands-On Neuroevolution with Python: Build High-Performing Artificial Neural Network Architectures Using Neuroevolution-based Algorithms
Hands-On Neuroevolution with Python: Build High-Performing Artificial Neural Network Architectures Using Neuroevolution-based Algorithms
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Hands-On Neuroevolution with Python: Build High-Performing Artificial Neural Network Architectures Using Neuroevolution-based Algorithms

ISBN: 183882491X ;ISBN: 9781838824914 ;EISBN: 1838822003 ;EISBN: 9781838822002 ;OCLC: 1134854915

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5
A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks
A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks
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A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks

IEEE transaction on neural networks and learning systems, 2014-07, Vol.25 (7), p.1229-1262

ISSN: 2162-237X ;EISSN: 2162-2388 ;DOI: 10.1109/TNNLS.2014.2317880 ;CODEN: ITNNAL

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6
Review of Deep Learning Algorithms and Architectures
Review of Deep Learning Algorithms and Architectures
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Review of Deep Learning Algorithms and Architectures

IEEE access, 2019, Vol.7, p.53040-53065 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2019.2912200 ;CODEN: IAECCG

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7
Neural networks and animal behavior
Neural networks and animal behavior
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Neural networks and animal behavior

2005 Princeton University Press ;ISBN: 9780691096339 ;ISBN: 0691096325 ;ISBN: 9780691096322 ;ISBN: 0691096333 ;EISBN: 1400850789 ;EISBN: 9781400850785 ;DOI: 10.1515/9781400850785 ;OCLC: 864383228 ;LCCallNum: QL751.65.D37

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8
Brain-Inspired Learning on Neuromorphic Substrates
Brain-Inspired Learning on Neuromorphic Substrates
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Brain-Inspired Learning on Neuromorphic Substrates

Proceedings of the IEEE, 2021-05, Vol.109 (5), p.935-950 [Peer Reviewed Journal]

ISSN: 0018-9219 ;EISSN: 1558-2256 ;DOI: 10.1109/JPROC.2020.3045625 ;CODEN: IEEPAD

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9
Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things
Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things
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Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things

IEEE access, 2017-01, Vol.5, p.18042-18050 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2017.2747560 ;CODEN: IAECCG

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10
Learning to Understand Remote Sensing Images: Volume 2
Learning to Understand Remote Sensing Images: Volume 2
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Learning to Understand Remote Sensing Images: Volume 2

https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode ;ISBN: 3038976997 ;ISBN: 9783038976998 ;DOI: 10.3390/books978-3-03897-699-8

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11
LSTM Fully Convolutional Networks for Time Series Classification
LSTM Fully Convolutional Networks for Time Series Classification
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LSTM Fully Convolutional Networks for Time Series Classification

IEEE access, 2018-01, Vol.6, p.1662-1669 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2017.2779939 ;CODEN: IAECCG

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12
Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art
Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art
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Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

IEEE transactions on microwave theory and techniques, 2022-11, Vol.70 (11), p.4597-4619 [Peer Reviewed Journal]

ISSN: 0018-9480 ;EISSN: 1557-9670 ;DOI: 10.1109/TMTT.2022.3197751 ;CODEN: IETMAB

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13
Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification
Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification
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Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification

IEEE access, 2018-01, Vol.6, p.36188-36197 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2018.2846685 ;CODEN: IAECCG

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14
Convolutional Recurrent Deep Learning Model for Sentence Classification
Convolutional Recurrent Deep Learning Model for Sentence Classification
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Convolutional Recurrent Deep Learning Model for Sentence Classification

IEEE access, 2018-01, Vol.6, p.13949-13957 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2018.2814818 ;CODEN: IAECCG

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15
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
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Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

IEEE transactions on geoscience and remote sensing, 2019-02, Vol.57 (2), p.924-935 [Peer Reviewed Journal]

ISSN: 0196-2892 ;EISSN: 1558-0644 ;DOI: 10.1109/TGRS.2018.2863224 ;CODEN: IGRSD2

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16
Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks
Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks
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Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors (Basel, Switzerland), 2021-07, Vol.21 (15), p.4953 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 by the authors. 2021 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s21154953 ;PMID: 34372189

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17
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
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Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features

IEEE access, 2018-01, Vol.6, p.1155-1166 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2017.2778011 ;CODEN: IAECCG

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18
A State-of-the-Art Survey on Deep Learning Theory and Architectures
A State-of-the-Art Survey on Deep Learning Theory and Architectures
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A State-of-the-Art Survey on Deep Learning Theory and Architectures

Electronics (Basel), 2019-03, Vol.8 (3), p.292 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2079-9292 ;EISSN: 2079-9292 ;DOI: 10.3390/electronics8030292

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19
Correction: Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases
Correction: Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases
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Correction: Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases

PloS one, 2023-12, Vol.18 (12), p.e0296316-e0296316 [Peer Reviewed Journal]

Copyright: © 2023 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ;COPYRIGHT 2023 Public Library of Science ;2023 Liu et al 2023 Liu et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0296316 ;PMID: 38113244

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20
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
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Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

Remote sensing (Basel, Switzerland), 2019-01, Vol.11 (2), p.196 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11020196

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