Livres : « DEEP LEARNING MODEL »

1

Poonkuntran, S., Balamurugan Balusamy et Rajesh Kumar Dhanraj. Object Detection with Deep Learning Models. Boca Raton : Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003206736.

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Deep Learning with Python : Learn Best Practices of Deep Learning Models with PyTorch. New York : Apress L. P., 2021.

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Bisong, Ekaba. Building Machine Learning and Deep Learning Models on Google Cloud Platform. Berkeley, CA : Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8.

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Paper, David. State-of-the-Art Deep Learning Models in TensorFlow. Berkeley, CA : Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7341-8.

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5

CTS student online assessment pilot study : An exploration of The Learning Manager (TLM) Model with Red Deer College. Edmonton, AB : Alberta Education, 2009.

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6

El-Amir, Hisham, et Mahmoud Hamdy. Deep Learning Pipeline : Building a Deep Learning Model with TensorFlow. Apress, 2019.

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Lattery, Mark J. Deep Learning in Introductory Physics : Exploratory Studies of Model-Based Reasoning. Information Age Publishing, 2016.

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8

Urtāns, Ēvalds. Function shaping in deep learning. RTU Press, 2021. http://dx.doi.org/10.7250/9789934226854.

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This work describes the importance of loss functions and related methods for deep reinforcement learning and deep metric learning. A novel MDQN loss function outperformed DDQN loss function in PLE computer game environments, and a novel Exponential Triplet loss function outperformed the Triplet loss function in the face re-identification task with VGGFace2 dataset reaching 85,7 % accuracy using zero-shot setting. This work also presents a novel UNet-RNN-Skip model to improve the performance of the value function for path planning tasks.

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1st, Kala K. U., et Nandhini M. 2nd. Deep Learning Model for Categorical Context Adaptation in Sequence-Aware Recommender Systems. INSC International Publisher (IIP), 2021.

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Jena, Om Prakash, Alok Ranjan Tripathy, Brojo Kishore Mishra et Ahmed A. Elngar, dir. Augmented Intelligence : Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150404011220301.

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Blockchain, whether public or private, is capable enough to maintain the integrity of transactions by decentralizing the records for users. Many IoT companies are using blockchain technology to make the world a better-connected place. Businesses and researchers are exploring ways to make this technology increasingly efficient for IoT services. This volume presents the recent advances in these two technologies. Chapters explain the fundamentals of Blockchain and IoT, before explaining how these technologies, when merged together, provide a transparent, reliable, and secure model for data processing by intelligent devices in various domains. Readers will be able to understand how these technologies are making an impact on healthcare, supply chain management and electronic voting, to give a few examples. The 10 peer-reviewed book chapters have been contributed by scholars, researchers, academicians, and engineering professionals, and provide a comprehensive yet easily digestible update on Blockchain on IoT technology.

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3D Deep Learning with Python : Design and Develop Your Computer Vision Model with 3D Data Using PyTorch3D and More. Packt Publishing, Limited, 2022.

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Leadership, Management and Data Science : Risk Calculations, Financial Services, Credit Scoring, Agriculture, Risk, Credit Risk Scoring Model,TR6 Scorecard, Alternative Credit, Loan, Deep Learning Techniques. Independently Published, 2022.

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Mire, Archana, Amit Kumar Tyagi et Shaveta Malik. Advanced Analytics and Deep Learning Models. Wiley & Sons, Incorporated, John, 2022.

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Raff, Edward. Inside Deep Learning : Math, Algorithms, Models. Manning Publications Co. LLC, 2022.

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Deep Learning Models for Medical Imaging. Elsevier, 2022. http://dx.doi.org/10.1016/c2020-0-00344-0.

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Das, Nibaran, K. C. Santosh et Swarnendu Ghosh. Deep Learning Models for Medical Imaging. Elsevier Science & Technology Books, 2021.

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Das, Nibaran, K. C. Santosh et Swarnendu Ghosh. Deep Learning Models for Medical Imaging. Elsevier Science & Technology, 2021.

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Mire, Archana, Amit Kumar Tyagi et Shaveta Malik. Advanced Analytics and Deep Learning Models. Wiley & Sons, Incorporated, John, 2022.

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Mire, Archana, Amit Kumar Tyagi et Shaveta Malik. Advanced Analytics and Deep Learning Models. Wiley & Sons, Incorporated, John, 2022.

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Mire, Archana, Amit Kumar Tyagi et Shaveta Malik. Advanced Analytics and Deep Learning Models. Wiley & Sons, Limited, John, 2022.

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Raff, Edward. Inside Deep Learning : Math, Algorithms, Models. Manning Publications Co. LLC, 2022.

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22

Benois-Pineau, Jenny, et Akka Zemmari. Multi-Faceted Deep Learning : Models and Data. Springer International Publishing AG, 2021.

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Benois-Pineau, Jenny, et Akka Zemmari. Multi-Faceted Deep Learning : Models and Data. Springer International Publishing AG, 2022.

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Singh, Pramod. Learn PySpark : Build Python-based Machine Learning and Deep Learning Models. Apress, 2019.

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25

Klingler-Vidra, Robyn. The Venture Capital State. Cornell University Press, 2018. http://dx.doi.org/10.7591/cornell/9781501723377.001.0001.

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The Venture Capital State investigates the diffusion of the globally acclaimed Silicon Valley venture capital (VC) policy model. The spread of this model has been ubiquitous, with at least 45 states across a range of countries, in terms of geography, culture, and size, attempting to build local VC markets. In contrast to the transcendent exuberance for VC, policymakers in each and every state have implemented a distinct set of policies. Even states of similar population and economic sizes that are geographically and culturally proximate, and at comparable levels of industrialization, have not implemented similar policies. This book explains why: policymakers are “contextually rational” in their learning; their context-rooted norms shape preferences, underpinning their distinct valuations of studied models. The normative context of those learning about the policy – how they see themselves and what they deem as locally appropriate – informs their design. Findings are based upon deep investigations of VC policymaking in an East Asian cluster of states: Hong Kong, Taiwan, and Singapore. These states’ VC successes reflects their ability to effectively adapt the highly-lauded model for their local context, not their policymakers’ approximation of the Silicon Valley policy model.

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Pal, Sujit, Amita Kapoor, Antonio Gulli et François Chollet. Deep Learning with TensorFlow and Keras : Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. Packt Publishing, Limited, 2022.

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27

Poonkuntran, S., Rajesh Kumar Dhanraj et Balamurugan Balusamy. Object Detection with Deep Learning Models : Principles and Applications. Taylor & Francis Group, 2022.

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28

Poonkuntran, S., Rajesh Kumar Dhanraj et Balamurugan Balusamy. Object Detection with Deep Learning Models : Principles and Applications. CRC Press LLC, 2022.

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29

IJSMI, Editor. Deep Learning Models Explored with Help of Python Programming. Independently Published, 2020.

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30

Poonkuntran, S., Rajesh Kumar Dhanraj et Balamurugan Balusamy. Object Detection with Deep Learning Models : Principles and Applications. Taylor & Francis Group, 2022.

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31

Poonkuntran, S., Rajesh Kumar Dhanraj et Balamurugan Balusamy. Object Detection with Deep Learning Models : Principles and Applications. CRC Press LLC, 2022.

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32

Poonkuntran, S., Rajesh Kumar Dhanraj et Balamurugan Balusamy. Object Detection with Deep Learning Models : Principles and Applications. Taylor & Francis Group, 2022.

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33

Kumar, Rahul, Matthew Lamons et Abhishek Nagaraja. Python Deep Learning Projects : 9 projects demystifying neural network and deep learning models for building intelligent systems. Packt Publishing, 2018.

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34

Singh, Pramod, et Avinash Manure. Learn TensorFlow 2.0 : Implement Machine Learning and Deep Learning Models with Python. Apress, 2020.

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35

Singh, Pramod, et Avinash Manure. Learn TensorFlow 2.0 : Implement Machine Learning and Deep Learning Models with Python. Apress, 2020.

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36

II, Taweh Beysolow. Introduction to Deep Learning Using R : A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R. Apress, 2017.

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37

Accelerate Deep Learning Workloads with Amazon SageMaker : Train, Deploy, and Scale Deep Learning Models Effectively Using Amazon SageMaker. Packt Publishing, Limited, 2022.

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38

Atkinson, Garin. Deep Learning with Pytorch Lightning : Build, Train, Deploy, and Scale Deep Learning Models Quickly and Accurately, Improving Productivity. Independently Published, 2022.

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39

Feyzkhanov, Rustem. Hands-On Serverless Deep Learning with TensorFlow and AWS Lambda : Training Serverless Deep Learning Models Using the AWS Infrastructure. Packt Publishing, Limited, 2019.

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40

Agrawal, Tanay. Hyperparameter Optimization in Machine Learning : Make Your Machine Learning and Deep Learning Models More Efficient. Apress L. P., 2020.

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Practical Convolutional Neural Networks : Implement advanced deep learning models using Python. Packt Publishing - ebooks Account, 2018.

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42

Machine Learning with Pytorch and Scikit-Learn : Develop Machine Learning and Deep Learning Models with Python. Packt Publishing, Limited, 2022.

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Palczewski, Tomasz, Jaejun (Brandon) Lee et Lenin Mookiah. Production-Ready Applied Deep Learning : Learn How to Construct and Deploy Complex Models in Pytorch and TensorFlow Deep Learning Frameworks. Packt Publishing, Limited, 2022.

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44

Huff, Stephen. Graph Models for Deep Learning : An Executive Review of Hot Technology. Independently Published, 2018.

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45

Khan, Shahnawaz, Monika Mangla, Nonita Sharma, Vaishali Wadhwa et Thirunavukkarasu K. Emerging Technologies for Healthcare : Internet of Things and Deep Learning Models. Wiley & Sons, Limited, John, 2021.

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46

Khan, Shahnawaz, Monika Mangla, Nonita Sharma, Vaishali Wadhwa et Thirunavukkarasu K. Emerging Technologies for Healthcare : Internet of Things and Deep Learning Models. Wiley & Sons, Incorporated, John, 2021.

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47

Mangla, Monika, Nonita Sharma, Vaishali Wadhwa, Thirunavukkarasu K et Poonam Garg. Emerging Technologies for Healthcare : Internet of Things and Deep Learning Models. Wiley & Sons, Incorporated, John, 2021.

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Mangla, Monika, Nonita Sharma, Vaishali Wadhwa, Thirunavukkarasu K et Poonam Garg. Emerging Technologies for Healthcare : Internet of Things and Deep Learning Models. Wiley & Sons, Incorporated, John, 2021.

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49

Machine Learning for Economics and Finance in TensorFlow 2 : Deep Learning Models for Empirical Work. Apress L. P., 2020.

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50

R Deep Learning Essentials : A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet, 2nd Edition. Packt Publishing, 2018.

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