The mathematical concepts that must be understood and applied in the machine trades and manufacturing are presented in clear, real world terms in the new edition of this best selling book. The understanding of those concepts is stressed in both the presentation and application in all topics, from si
Mathematics for Machine Technology
β Scribed by Robert D. Smith
- Publisher
- Delmar Cengage Learning
- Year
- 1998
- Tongue
- English
- Leaves
- 495
- Edition
- 4
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
The mathematical concepts that must be understood and applied in the machine trades and manufacturing are presented in clear, real world terms in the new edition of this best selling book. The understanding of those concepts is stressed in both the presentation and application in all topics, from simple to complex. The presentation of basic concepts is accompanied by realistic, industry related examples, illustrations, and actual applications. Drawings, rather than words, often inform and motivate the user. Algebraic and geometric principles are carefully sequenced and integrated with trigonometric applications.
β¦ Subjects
ΠΠ°ΡΠΈΠ½ΠΎΡΡΡΠΎΠ΅Π½ΠΈΠ΅ ΠΈ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ°;Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΡ ΠΌΠ°ΡΠΈΠ½ΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ;
π SIMILAR VOLUMES
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The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or compute
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