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Advances in Computational Intelligence

forecasting; computational intelligence methods for time series; human activity recognition; new and future tendencies in brain-computer interface;

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Advances in Computational Intelligence

forecasting; computational intelligence methods for time series; human activity recognition; new and future tendencies in brain-computer interface;

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Deep Learning Techniques and Optimization Strategies in Big Data Analytics

, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning;

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Computational Science ICCS 2021

of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations;

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Artificial Intelligence for Automated Pricing Based on Product Descriptions

and deep learning methods for pricing, including from regression methods to hybrid and ensemble methods. The computational experiments are;

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Deep Learning and Linguistic Representation

of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the;

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Deep Learning and Linguistic Representation

of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the;

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Computational Collective Intelligence

processing; low resource languages processing; computational collective intelligence and natural language processing; computational intelligence for;

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Handbook of Deep Learning in Biomedical Engineering

composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the;

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Computational Methods for Deep Learning

-learning toolboxes for implementing deep learning algorithms. As a prerequisite, readers should have a solid understanding especially;

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Computational Methods for Deep Learning

-learning toolboxes for implementing deep learning algorithms. As a prerequisite, readers should have a solid understanding especially;

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Deep Excavations in Soil

and computational numerical methods. It is heavily illustrated with practical applications and case histories for practising engineers, and;

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Machine Learning for Subsurface Characterization

, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods;

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Computational Intelligence Methods for Super-Resolution in Image Processing Applications

This book explores the application of deep learning techniques within a particularly difficult computational type of computer vision (CV;

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Inventive Computation Technologies

, machine learning, scientific computing, and computational vision. A vast number of intelligent computational algorithms are emerging, along with;

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Advances in Computational Intelligence Systems

, on 4-6 September 2019. The book offers a valuable reference guide for readers with expertise in computational intelligence or who are seeking;

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Trends in Deep Learning Methodologies

powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large;

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Computational Intelligence Methods for Bioinformatics and Biostatistics

This book constitutes revised selected papers from the 16th International Meeting on Computational Intelligence Methods for Bioinformatics;

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Deep Learning and Missing Data in Engineering Systems

undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying;

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Modern Deep Learning Design and Application Development

helpful tips and best practices for making the most of the framework. Next, yoĆ¼ll learn how to train models effectively with transfer learning and;

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Computational Science ICCS 2021

of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations;

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Big Data in Omics and Imaging, Two Volume Set

FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data;

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Natural Language Processing with PyTorchlow

Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as;

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Neural Information Processing Part I

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Neural Information Processing Part II

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Neural Information Processing Part III

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Neural Information Processing Part IV

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Einde inhoud

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