Tag: Methods

Computational Methods with MATLAB®


Free Download Computational Methods with MATLAB®
English | 2024 | ISBN: 3031404777 | 308 Pages | PDF EPUB (True) | 34 MB
This textbook provides readers a comprehensive introduction to numerical methods, using MATLAB®. The authors discuss the theory and application of the most often used numerical methods, using MATLAB as a computational tool. The book is designed to be accessible to readers of varying backgrounds, so the presentation focuses more on the description, implementation, and application of the methods and less on the mathematical details. This book not only covers the most important methods and techniques of scientific computation, but also contains a great amount of code and implementations, facilitating the process of learning and application.

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Computational Methods for Deep Learning (2nd Edition)


Free Download Computational Methods for Deep Learning: Theory, Algorithms, and Implementations
English | 2023 | ISBN: 9819948223 | 467 Pages | PDF EPUB (True) | 25 MB
The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book.

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Ensemble Methods for Machine Learning, Video Edition


Free Download Ensemble Methods for Machine Learning, Video Edition
Released 5/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 11h 5m | Size: 1.52 GB
Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate.

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Wavelet Methods – Elliptic Boundary Value Problems and Control Problems


Free Download Wavelet Methods – Elliptic Boundary Value Problems and Control Problems by Angela Kunoth
English | PDF | 2001 | 150 Pages | ISBN : 3519003279 | 18.6 MB
While wavelets have since their discovery mainly been applied to problems in signal analysis and image compression, their analytic power has more and more also been recognized for problems in Numerical Analysis. Together with the functional analytic framework for different differential and integral quations, one has been able to conceptu ally discuss questions which are relevant for the fast numerical solution of such problems: preconditioning issues, derivation of stable discretizations, compression of fully popu lated matrices, evaluation of non-integer or negative norms, and adaptive refinements based on A-posteriori error estimators. This research monograph focusses on applying wavelet methods to elliptic differential equations. Particular emphasis is placed on the treatment of the boundary and the boundary conditions. Moreover, a control problem with an elliptic boundary problem as contraint serves as an example to show the conceptual strengths of wavelet techniques for some of the above mentioned issues. At this point, I would like to express my gratitude to several people before and during the process of writing this monograph. Most of all, I wish to thank Prof. Dr. Wolfgang Dahmen to whom I personally owe very much and with whom I have co-authored a large part of my work. He is responsible for the very stimulating and challenging scientific atmosphere at the Institut fiir Geometrie und Praktische Mathematik, RWTH Aachen. We also had an enjoyable collaboration with Prof. Dr. Reinhold Schneider from the Technical University of Chemnitz.

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Statistical Methods in Bioinformatics An Introduction


Free Download Statistical Methods in Bioinformatics: An Introduction by Warren J. Ewens , Gregory Grant
English | PDF | 2005 | 616 Pages | ISBN : 0387400826 | 4.6 MB
Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.

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