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Paul Kurowski ... 600 pages - Publisher: SDC Publications; (March, 2018) ... Language: English - ISBN-10: 1630571539 - ISBN-13: 978-1630571535.

Engineering Analysis with SOLIDWORKS Simulation 2018 goes beyond the standard software manual. Its unique approach concurrently introduces you to the SOLIDWORKS Simulation 2018 software and the fundamentals of Finite Element Analysis (FEA) through hands-on exercises. A number of projects are presented using commonly used parts to illustrate the analysis features of SOLIDWORKS Simulation. Each chapter is designed to build on the skills, experiences and understanding gained from the previous chapters.

Mark Fenner ... 592 pages - Publisher: Addison-Wesley Professional; (August, 2019) ... Language: English - ISBN-10: 0134845625 - ISBN-13: 978-0134845623.

The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python: Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.

Understand machine learning algorithms, models, and core machine learning concepts + Classify examples with classifiers, and quantify examples with regressors + Realistically assess performance of machine learning systems + Use feature engineering to smooth rough data into useful forms + Chain multiple components into one system and tune its performance + Apply machine learning techniques to images and text + Connect the core concepts to neural networks and graphical models + Leverage the Python scikit-learn library and other powerful tools.

Bofang Zhu ... 822 pages - Publisher: Wiley; (March, 2018) ... Language: English - AmazonSIN: B07C8HGH2Z.

A comprehensive review of the Finite Element Method (FEM), this book provides the fundamentals together with a wide range of applications in civil, mechanical and aeronautical engineering. It addresses both the theoretical and numerical implementation aspects of the FEM, providing examples in several important topics such as solid mechanics, fluid mechanics and heat transfer, appealing to a wide range of engineering disciplines. Written by a renowned author and academician with the Chinese Academy of Engineering, The Finite Element Method would appeal to researchers looking to understand how the fundamentals of the FEM can be applied in other disciplines. Researchers and graduate students studying hydraulic, mechanical and civil engineering will find it a practical reference text.

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