Exploring Mathematics Book for Class 4
Author: Rashmi Katyal
Publisher: Goyal Brothers Prakashan
Published: 2020-04-15
Total Pages: 232
ISBN-13: 9389693810
DOWNLOAD EBOOKGoyal Brothers Prakashan
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Author: Rashmi Katyal
Publisher: Goyal Brothers Prakashan
Published: 2020-04-15
Total Pages: 232
ISBN-13: 9389693810
DOWNLOAD EBOOKGoyal Brothers Prakashan
Author: Rashmi Katyal
Publisher: Goyal Brothers Prakashan
Published: 2021-04-01
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Rashmi Katyal
Publisher: Goyal Brothers Prakashan
Published: 2021-04-01
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Rashmi Katyal
Publisher: Goyal Brothers Prakashan
Published: 2021-04-01
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Rashmi Katyal
Publisher: Goyal Brothers Prakashan
Published: 2021-04-01
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Rashmi Katyal
Publisher: Goyal Brothers Prakashan
Published: 2021-04-01
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Rashmi Katyal
Publisher: Goyal Brothers Prakashan
Published: 2020-04-08
Total Pages:
ISBN-13: 9389693845
DOWNLOAD EBOOKGoyal Brothers Prakashan
Author: Rashmi Katyal
Publisher: Goyal Brothers Prakashan
Published: 2020-03-11
Total Pages:
ISBN-13: 9389693837
DOWNLOAD EBOOKGoyal Brothers Prakashan
Author: Rashmi Katyal
Publisher: Goyal Brothers Prakashan
Published: 2020-04-15
Total Pages:
ISBN-13: 9389693853
DOWNLOAD EBOOKGoyal Brothers Prakashan
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
Published: 2020-04-23
Total Pages: 392
ISBN-13: 1108569323
DOWNLOAD EBOOKThe 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 computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.