Pyzer-Knapp Deep Learning for Physical Scientists: Acceleratin g Research with Machine Learning 9781119408338

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Автор: Pyzer-Knapp
Название: Deep Learning for Physical Scientists: Acceleratin g Research with Machine Learning
Перевод названия: Пицер-Кнапп: Глубокое обучение учёных физиков. Ускорение исследований с помощью машинного обучения
ISBN: 9781119408338
Издательство: Wiley
Классификация:

Машинное обучение

ISBN-10: 1119408334
Обложка/Формат: Hardcover
Страницы: 144
Вес: 0.45 кг.
Дата издания: 29.07.2021
Язык: English
Размер: 229 x 152 x 13
Читательская аудитория: Professional & vocational
Основная тема: Computational Chemistry & Molecular Modeling
Подзаголовок: Accelerating research with machine learning
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field Deep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome. Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and problems.

Practical case studies and problems are presented, giving readers an opportunity to put what they have learned into practice, with exemplar coding approaches provided to assist the reader. From modelling basics to feed-forward networks, the book offers a broad cross-section of machine learning techniques to improve physical science research. Readers will also enjoy: A thorough introduction to the basic classification and regression with perceptrons An exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of training An examination of multi-layer perceptrons for learning from descriptors and de-noising data Discussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from images A treatment of Bayesian optimization for tuning deep learning architectures Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access.

Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including:*Basic classification and regression with perceptrons *Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training*Multi-Layer Perceptrons for learning from descriptors, and de-noising data*Recurrent neural networks for learning from sequences*Convolutional neural networks for learning from images*Bayesian optimization for tuning deep learning architecturesEach of these areas has direct application to physical science research, and by the end of the book, the reader should feel comfortable enough to select the methodology which is best for their situation, and be able to implement and interpret outcome of the deep learning model.

The book is designed to teach researchers to think in new ways, providing them with new avenues to attack problems, and avoid roadblocks within their research. This is achieved through the inclusion of case-study like problems at the end of each chapter, which will give the reader a chance to practice what they have just learnt in a close-to-real-world setting, with example solutions provided through an online resource. Market Description This book introduces the reader to the transformative techniques involved in deep learning.

A range of methodologies are addressed including: * Basic classification and regression with perceptrons* Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training* Multi-Layer Perceptrons for learning from descriptors, and de-noising data* Recurrent neural networks for learning from sequences* Convolutional neural networks for learning from images* Bayesian optimization for tuning deep learning architectures Each of these areas has direct application to physical science research, and by the end of the book, the reader should feel comfortable enough to select the methodology which is best for their situation, and be able to implement and interpret outcome of the deep learning model. The book is designed to teach researchers to think in new ways, providing them with new avenues to attack problems, and avoid roadblocks within their research. This is achieved through the inclusion of case-study like problems at the end of each chapter, which will give the reader a chance to practice what they have just learnt in a close-to-real-world setting, with example solutions provided through an online resource.

Deep Learning for Physical Scientists: Acceleratin g Research with Machine Learning, Pyzer-Knapp