Author: DAVID. SPENCERPublisher:
ISBN: 9781786325273
Category :
Languages : en
Pages :
Book Description
No other description available.
Gateway B1 Student Book
Author: David SpencerPublisher:
ISBN: 9780230723443
Category : English language
Languages : en
Pages : 160
Book Description
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.
Author: Gill HolleyGateway B1
Author: Anna ColePublisher:
ISBN: 9780230417229
Category :
Languages : en
Pages :
Book Description
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.
Author: David SpencerGateway B1
Author: Annie CornfordPublisher: Macmillan Elt
ISBN: 9780230723511
Category : Juvenile Nonfiction
Languages : en
Pages : 128
Book Description
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
Author: Marc Peter DeisenrothPublisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
Book Description
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.
Author: Lynda Edwards
Author: David Spencer
Author: Liz Hocking