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Books > Science & Mathematics > Astronomy, space & time
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Machine Learning for Physics and Astronomy (Paperback)
Loot Price: R940
Discovery Miles 9 400
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Machine Learning for Physics and Astronomy (Paperback)
Expected to ship within 10 - 15 working days
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A hands-on introduction to machine learning and its applications to
the physical sciences As the size and complexity of data continue
to grow exponentially across the physical sciences, machine
learning is helping scientists to sift through and analyze this
information while driving breathtaking advances in quantum physics,
astronomy, cosmology, and beyond. This incisive textbook covers the
basics of building, diagnosing, optimizing, and deploying machine
learning methods to solve research problems in physics and
astronomy, with an emphasis on critical thinking and the scientific
method. Using a hands-on approach to learning, Machine Learning for
Physics and Astronomy draws on real-world, publicly available data
as well as examples taken directly from the frontiers of research,
from identifying galaxy morphology from images to identifying the
signature of standard model particles in simulations at the Large
Hadron Collider. Introduces readers to best practices in
data-driven problem-solving, from preliminary data exploration and
cleaning to selecting the best method for a given task Each chapter
is accompanied by Jupyter Notebook worksheets in Python that enable
students to explore key concepts Includes a wealth of review
questions and quizzes Ideal for advanced undergraduate and early
graduate students in STEM disciplines such as physics, computer
science, engineering, and applied mathematics Accessible to
self-learners with a basic knowledge of linear algebra and calculus
Slides and assessment questions (available only to instructors)
General
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