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Deep Learning for Physical Scientists

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Accelerating Research with Machine Learning

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ISBN: 9781119408338 Category:

<p><b>Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field&nbsp;</b>&nbsp;</p> <p><i>Deep Learning for Physical Scientists: Accelerating Research with Machine Learning</i>&nbsp;delivers&nbsp;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.&nbsp;</p> <p>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&nbsp;put what they have learned into practice, with exemplar coding approaches provided to assist the reader.&nbsp;&nbsp;</p> <p>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:&nbsp;</p> <ul style=”margin-bottom: 0in; font-size: medium; margin-top: 0in;” type=”disc”> <li class=”MsoListParagraph” style=”margin: 0in; font-size: 11pt; font-family: Calibri, sans-serif;”>A thorough introduction to the basic classification and regression with perceptrons&nbsp;</li> <li class=”MsoListParagraph” style=”margin: 0in; font-size: 11pt; font-family: Calibri, sans-serif;”>An exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of training&nbsp;</li> <li class=”MsoListParagraph” style=”margin: 0in; font-size: 11pt; font-family: Calibri, sans-serif;”>An examination of multi-layer perceptrons for learning from descriptors and de-noising data&nbsp;</li> <li class=”MsoListParagraph” style=”margin: 0in; font-size: 11pt; font-family: Calibri, sans-serif;”>Discussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from images&nbsp;</li> <li class=”MsoListParagraph” style=”margin: 0in; font-size: 11pt; font-family: Calibri, sans-serif;”>A treatment of Bayesian optimization for tuning deep learning architectures&nbsp;</li> </ul> <p>Perfect for academic and industrial research professionals in the physical&nbsp;sciences,&nbsp;<i>Deep Learning for Physical Scientists: Accelerating Research with Machine Learning</i>&nbsp;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.&nbsp;</p> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>Perfect for academic and industrial research professionals in the physical&nbsp;sciences,&nbsp;<em style=”font-family: Calibri, sans-serif; font-size: 11pt;”>Deep Learning for Physical Scientists: Accelerating Research with Machine Learning&nbsp;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.&nbsp;&nbsp;</i></div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&nbsp;</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including:</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull;Basic classification and regression with perceptrons&nbsp;</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull;Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull;Multi-Layer Perceptrons for learning from descriptors, and de-noising data</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull;Recurrent neural networks for learning from sequences</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull;Convolutional neural networks for learning from images</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull;Bayesian optimization for tuning deep learning architectures</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>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.&nbsp; 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.&nbsp; 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 &lsquo;solutions&rsquo; provided through an online resource.&nbsp; &nbsp;</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&nbsp;</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>Market Description</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&nbsp;</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including:</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&nbsp;</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull; Basic classification and regression with perceptrons</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull; Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull; Multi-Layer Perceptrons for learning from descriptors, and de-noising data</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull; Recurrent neural networks for learning from sequences</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull; Convolutional neural networks for learning from images</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&bull; Bayesian optimization for tuning deep learning architectures</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>&nbsp;</div> <div id=”_mcePaste” style=”position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;”>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.&nbsp; 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.&nbsp; 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 &lsquo;solutions&rsquo; provided through an online resource.&nbsp;&nbsp;</div>

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