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This isn’t a conventional guide.
This can be a monograph; the main focus subject being the best way to allow mechanical and aerospace engineers to finish machine studying tasks on simulation information, from begin to end.
Who this guide is for: That is an abstraction of experiences right into a sensible information to get CFD/CAE practitioners extra snug in machine studying tasks. After a whole lot of requests for assist, I felt the conviction to put aside my nights for six months and produce this guide as a extra scalable means to assist.
This guide has plenty of code (not shareable on Github). There may be an abundance of sources that cowl theoretical data of machine studying in ‘the mainstream’, however comparatively little by comparability for CAE functions (particularly few which can be hands-on). My hope is that the reader already has some (very minimal) theoretical data once they choose this guide up. There can be some clarification on the algorithms with examples (in Python), and some extent of surveying/summarizing widespread ones, however the main focus is how and what you must do to unravel machine studying issues. That is what I consult with because the pipeline of steps from begin to end in a machine studying venture, which appears to have a steep studying curve (my motivation for scripting this guide). This guide will even share my really useful studying pathway for CFD/CAE engineers to develop their AI/ML expertise and portfolios and is nice for freshmen.
I’m a fan of the ‘code alongside’ strategy and take that to coronary heart on this guide. I like to recommend studying the guide whereas logged into a pc the place you’ll be able to code.
Concerning the Creator: Whereas I grew up in a turbomachinery lab characterizing warmth switch, fluid mechanics, and turbulence in fuel turbine secondary circulation programs in graduate college, I fell in love with synthetic intelligence in 2017 engaged on a venture that mixed computational fluid dynamics simulations and machine studying throughout an internship with the Siemens Healthineers in Princeton NJ. Ever since, I’ve sought to keep up my profession route (mechanical and aerospace engineering functions) however incorporate machine studying and information science as a method to enhance our numerical strategies in engineering.
ASIN : B0CZF4YN31
Writer : Independently printed (March 26, 2024)
Language : English
Paperback : 168 pages
ISBN-13 : 979-8878702317
Merchandise Weight : 11 ounces
Dimensions : 6 x 0.38 x 9 inches
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