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 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. 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.
