Machine Learning for Physics and Astronomy
暫譯: 物理與天文學的機器學習
Acquaviva, Viviana
- 出版商: Princeton University Press
- 出版日期: 2023-08-15
- 售價: $2,070
- 貴賓價: 9.8 折 $2,029
- 語言: 英文
- 頁數: 280
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0691206414
- ISBN-13: 9780691206417
-
相關分類:
Machine Learning、物理學 Physics
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商品描述
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)
商品描述(中文翻譯)
機器學習及其在物理科學中的應用的實作入門
隨著物理科學中數據的大小和複雜性持續以指數增長,機器學習正在幫助科學家篩選和分析這些信息,同時推動量子物理、天文學、宇宙學等領域的驚人進展。本書深入淺出地介紹了構建、診斷、優化和部署機器學習方法以解決物理和天文學研究問題的基本知識,強調批判性思維和科學方法。透過實作學習的方式,《Machine Learning for Physics and Astronomy》利用真實的公開數據以及直接來自研究前沿的範例,從識別圖像中的星系形態到在大型強子對撞機的模擬中識別標準模型粒子的特徵。
- 向讀者介紹數據驅動問題解決的最佳實踐,從初步數據探索和清理到為特定任務選擇最佳方法
- 每章配有Python的Jupyter Notebook工作表,讓學生探索關鍵概念
- 包含大量的複習問題和測驗
- 適合物理、計算機科學、工程和應用數學等STEM學科的高年級本科生和早期研究生
- 對於具備基本線性代數和微積分知識的自學者也很友好
- 幻燈片和評估問題(僅供教師使用)
作者簡介
Viviana Acquaviva is professor of physics at the New York City College of Technology and the Graduate Center, City University of New York, and the recipient of a PIVOT fellowship to apply AI tools to problems in climate. She was named one of Italy's fifty most inspiring women in technology by InspiringFifty, which recognizes women in STEM who serve as role models for girls around the world.
作者簡介(中文翻譯)
Viviana Acquaviva 是紐約市科技學院及紐約市立大學研究中心的物理學教授,並獲得 PIVOT 獎學金以將人工智慧工具應用於氣候問題。她被 InspiringFifty 評選為意大利五十位最具啟發性的科技女性之一,該組織旨在表彰在 STEM 領域中作為全球女孩榜樣的女性。