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Time is a great teacher, but unfortunately it kills all its pupils.
Cambridge University Press. 1. Very Good. Very Good. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported Cambridge University Press ISBN 110845514x 9781108455145 [US]
Deisenroth, Marc Peter, and Faisal, A Aldo, and Ong, Cheng Soon
USD
43.58
Classy Store via Alibris /Alibris
New York Cambridge University Press 2020 Trade paperback New. New Trade paperback (US). Glued binding. 398 p. Contains: Unspecified, Halftones, black & white, Halftones, color. Audience: General/trade.
Deisenroth, Marc Peter, and Faisal, A Aldo, and Ong, Cheng Soon
USD
43.60
Alibris /Alibris
New York Cambridge University Press 2020 Trade paperback New. Trade paperback (US). Glued binding. 398 p. Contains: Unspecified, Halftones, black & white, Halftones, color.
Cambridge University Press, Date: 2020. Soft cover. Very Good. 7x0x10. Book has some intermittent underling and margin marks in pencil on the first 26 pages. Elsewhere, minor wear. ***PROMPT, PROFESSIONAL SERVICE!*** 2020. Cambridge University Press ISBN 110845514X 9781108455145 [US]
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover Book has some intermittent underling and margin marks in pencil on the first 26 pages. Elsewhere, minor wear. ***PROMPT, PROFESSIONAL SERVICE!*** [Broken Arrow, OK, U.S.A.] [Publication Year: 2020]
Deisenroth, Marc Peter|Faisal, A. Aldo|Ong, Cheng Soon
USD
45.38
moluna /ZVAB
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, . [Greven, Germany] [Publication Year: 2020]
Cambridge University Press 2020 Trade paperback New Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover First Edition Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes). [Columbia, MO, U.S.A.] [Publication Year: 1920]
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover New Book. Shipped from UK. Established seller since 2000. [Fairford, GLOS, United Kingdom] [Publication Year: 2020]
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. [Montgomery, IL, U.S.A.] [Publication Year: 2020]
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover SHIPS FROM USA. Used books have different signs of use and do not include supplemental materials such as CDs, Dvds, Access Codes, charts or any other extra material. All used books might have various degrees of writing, highliting and wear and tear and possibly be an ex-library with the usual stickers and stamps. Dust Jackets are not guaranteed and when still present, they will have various degrees of tear and damage. All images are Stock Photos, not of the actual item. [Portland, ME, U.S.A.] [Publication Year: 2020]
Paperback / softback. New. This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. ISBN 110845514x 9781108455145 [GB]
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover Ships SAME or NEXT business day. We Ship to APO/FPO addr. Choose EXPEDITED shipping and receive in 2-5 business days within the United States. See our member profile for customer support contact info. We have an easy return policy. [Grandview Heights, OH, U.S.A.] [Publication Year: 2020]
Deisenroth, Marc Peter|Faisal, A. Aldo|Ong, Cheng Soon
USD
50.03
moluna /AbebooksDE
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, . [Greven, Germany] [Publication Year: 2020]
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover Buy with confidence! Book is in good condition with minor wear to the pages, binding, and minor marks within [Amherst, NY, U.S.A.] [Publication Year: 2020]
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover New copy - Usually dispatched within 4 working days. [Southport, United Kingdom] [Publication Year: 2020]
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Pr. Apr 2020] Softcover Neuware -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. 370 pp. Englisch [Bergisch Gladbach, Germany] [Publication Year: 2020] ...
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Pr. Apr 2020] Softcover Neuware -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 studentsand otherswith a mathematical background, these derivations provide a starting point to machine learning texts. Forthoselearning 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. [Zwiesel, Germany] [Publication Year: 2020] ...
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Pr. Apr 2020] Softcover Neuware -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. 370 pp. Englisch [Bergisch Gladbach, Germany] [Publication Year: 2020] ...
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover New Book. Shipped from UK. Established seller since 2000. [Wood Dale, IL, U.S.A.] [Publication Year: 2020]
Cambridge University Press 4/23/2020 12: 00: 00 AM Softcover New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Please note we cannot offer an expedited shipping service from the UK.
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Pr. Apr 2020] Softcover Neuware - 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. [Einbeck, Germany] [Publication Year: 2020] ...
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Press] Softcover New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. [Irving, TX, U.S.A.] [Publication Year: 2020]
Cambridge University Press 4/23/2020 12: 00: 00 AM Softcover PLEASE NOTE, WE DO NOT SHIP TO DENMARK. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Please note we cannot offer an expedited shipping service from the UK.
Paperback / softback. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositio ISBN 110845514x 9781108455145 [GB]
Deisenroth, Marc Peter, and Faisal, A Aldo, and Ong, Cheng Soon
USD
57.48
Ria Christie Books via Alibris /Alibris
New York Cambridge University Press 2020 Trade paperback New. Trade paperback (US). Glued binding. 398 p. Contains: Unspecified, Halftones, black & white, Halftones, color.
Cambridge University Press 4/23/2020 12: 00: 00 AM Softcover PLEASE NOTE, WE DO NOT SHIP TO DENMARK. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Please note we cannot offer an expedited shipping service from the UK.
ISBN10: 110845514X, ISBN13: 9781108455145, [publisher: Cambridge University Pr. Apr 2020] Softcover Neuware - 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. [Einbeck, Germany] [Publication Year: 2020] ...
DISCLOSURE:
When you click on links to various merchants on this site and make a purchase, this can result in this site earning a commission at no extra cost to you. Affiliate programs and affiliations include, but are not limited to, the eBay Partner Network, Amazon and Alibris.
