Gateway B1 Student Book

Gateway B1 Student Book

Author: David Spencer

Publisher:

Published: 2011

Total Pages: 160

ISBN-13: 9780230723443

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Gateway is an academically-rich five-level course designed to lead teenage students to success in school-leaving and university entrance exams and prepare them for university and the world of work.


Gateway B1

Gateway B1

Author: Anna Cole

Publisher:

Published: 2011-01-01

Total Pages:

ISBN-13: 9780230417229

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Helps students prepare and practise for their school-leaving exams and equips them with lifelong learning and study skills. Taking an inductive approach to Grammar, this title revises and extends the students' knowledge of grammar and vocabulary through varied skills-based activities that recycle, revise, evaluate and develop language skills.


Gateway B1

Gateway B1

Author: Annie Cornford

Publisher: Macmillan Elt

Published: 2010-12-30

Total Pages: 128

ISBN-13: 9780230723511

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Helps students prepare and practise for their school-leaving exams and equips them with lifelong learning and study skills. Taking an inductive approach to Grammar, this title revises and extends the students' knowledge of grammar and vocabulary through varied skills-based activities that recycle, revise, evaluate and develop language skills.


Mathematics for Machine Learning

Mathematics for Machine Learning

Author: Marc Peter Deisenroth

Publisher: Cambridge University Press

Published: 2020-04-23

Total Pages: 392

ISBN-13: 1108569323

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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 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.