Sách Machine Learning for Physics and Astronomy

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Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

Sách Machine Learning for Physics and Astronomy

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Sách Machine Learning for Physics and Astronomy

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Mã sản phẩm:
Danh mục: Sách IT, Sách chuyên ngành
Tình trạng: Còn hàng

Mô tả

Sách Machine Learning for Physics and Astronomy

Sách keo gáy, bìa mềm

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)

Categories:Computers - Programming

Year:2023

Language:english

Pages:281

https://zalo.me/0969436832

0969436832