Description: Machine Learning for the Physical Sciences by Carlo Requião da Cunha Estimated delivery 3-12 business days Format Hardcover Condition Brand New Description This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. Publisher Description Machine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields.This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering, targeting senior undergraduate and graduate students in physics and electrical engineering, alongside advanced researchers.All codes are available on the authors website: C•Lab (nau.edu)They are also available on GitHub: Key Features:Includes detailed algorithms.Supplemented by codes in Julia: a high-performing language and one that is easy to read for those in the natural sciences.All algorithms are presented with a good mathematical background. Author Biography Carlo R. da Cunha is currently an assistant professor at the School of Informatics, Computing, and Cyber Systems at Northern Arizona University. He holds a Ph.D. degree in electrical engineering from Arizona State University. Throughout his career, Dr. da Cunha has held various academic positions and research affiliations in institutions such as McGill University, Chiba University, and the Technical University of Vienna. His research focuses on computational science, where he applies machine learning techniques to the design of innovative electronic devices and systems. Details ISBN 1032392290 ISBN-13 9781032392295 Title Machine Learning for the Physical Sciences Author Carlo Requião da Cunha Format Hardcover Year 2023 Pages 266 Publisher Taylor & Francis Ltd GE_Item_ID:157296971; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys
Price: 249.55 USD
Location: Fairfield, Ohio
End Time: 2024-10-16T06:03:43.000Z
Shipping Cost: 0 USD
Product Images
Item Specifics
Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money Back
ISBN-13: 9781032392295
Book Title: Machine Learning for the Physical Sciences
Number of Pages: 266 Pages
Publication Name: Machine Learning for the Physical Sciences : Fundamentals and Prototyping with Julia
Language: English
Publisher: Taylor & Francis Group
Publication Year: 2023
Subject: Applied Sciences, General
Item Weight: 24.4 Oz
Type: Textbook
Item Length: 9.2 in
Subject Area: Science
Author: Carlo Requião Da Cunha
Item Width: 6.1 in
Format: Hardcover
