Description: Please refer to the section BELOW (and NOT ABOVE) this line for the product details - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Title:Machine Learning For Engineers: Using Data To Solve Problems For Physical SystemsISBN13:9783030703875ISBN10:3030703878Author:McClarren, Ryan G. (Author)Description:All Engineers And Applied Scientists Will Need To Harness The Power Of Machine Learning To Solve The Highly Complex And Data Intensive Problems Now Emerging This Text Teaches State-Of-The-Art Machine Learning Technologies To Students And Practicing Engineers From The Traditionally Analog Disciplines--Mechanical, Aerospace, Chemical, Nuclear, And Civil Dr Mcclarren Examines These Technologies From An Engineering Perspective And Illustrates Their Specific Value To Engineers By Presenting Concrete Examples Based On Physical Systems The Book Proceeds From Basic Learning Models To Deep Neural Networks, Gradually Increasing Readers' Ability To Apply Modern Machine Learning Techniques To Their Current Work And To Prepare Them For Future, As Yet Unknown, Problems Rather Than Taking A Black Box Approach, The Author Teaches A Broad Range Of Techniques While Conveying The Kinds Of Problems Best Addressed By Each Examples And Case Studies In Controls, Dynamics, Heat Transfer, And Other Engineering Applications Are Implemented In Python And The Libraries Scikit-Learn And Tensorflow, Demonstrating How Readers Can Apply The Most Up-To-Date Methods To Their Own Problems The Book Equally Benefits Undergraduate Engineering Students Who Wish To Acquire The Skills Required By Future Employers, And Practicing Engineers Who Wish To Expand And Update Their Problem-Solving Toolkit Binding:Hardcover, HardcoverPublisher:SpringerPublication Date:2021-08-20Weight:0 lbsDimensions:Number of Pages:285Language:English
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Location: USA
End Time: 2024-11-22T01:42:24.000Z
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Book Title: Machine Learning for Engineers : Using Data to Solve Problems for Physical Systems
Item Length: 9.3in
Item Width: 6.1in
Author: Ryan G. Mcclarren
Format: Hardcover
Language: English
Topic: Engineering (General), Computer Science, Intelligence (Ai) & Semantics
Publisher: Springer International Publishing A&G
Publication Year: 2021
Genre: Technology & Engineering, Computers
Item Weight: 19.9 Oz
Number of Pages: Xiii, 247 Pages
