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001 9781003051220
003 FlBoTFG
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006 m d
007 cr |||||||||||
008 200818t20202021flua ob 000 0 eng d
040 _aOCoLC-P
_beng
_erda
_epn
_cOCoLC-P
020 _a9781000196993
_q(ePub ebook)
020 _a1000196992
_q(ePub ebook)
020 _a9781000196979
_q(PDF ebook)
020 _a1000196976
_q(PDF ebook)
020 _a9781000196986
_q(Mobipocket ebook)
020 _a1000196984
_q(Mobipocket ebook)
020 _a9781003051220
_q(ebook)
020 _a1003051227
_q(ebook)
020 _z9780367561949 (hbk.)
020 _z9780367507855 (pbk.)
024 7 _a10.1201/9781003051220
_2doi
035 _a(OCoLC)1198598560
035 _a(OCoLC-P)1198598560
050 4 _aQ325.5
_b.C53 2020eb
072 7 _aCOM
_x004000
_2bisacsh
072 7 _aCOM
_x051300
_2bisacsh
072 7 _aCOM
_x051210
_2bisacsh
072 7 _aUY
_2bicssc
082 0 4 _a006.31
_223
100 1 _aClaster, William B.,
_eauthor.
245 1 0 _aMathematics and R programming for machine learning :
_bfrom the ground up /
_cWilliam B. Claster.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c2020.
264 4 _c©2021
300 _a1 online resource :
_billustrations (black and white)
336 _atext
_2rdacontent
336 _astill image
_2rdacontent
337 _acomputer
_2rdamedia
338 _aonline resource
_2rdacarrier
505 0 _a<P>Chapter 1. Functions Tutorial. Chapter 2. Logic and R. Chapter 3. Sets with R: Building the Tools. Chapter 4. Probability. Chapter 5. Naïve Rule. Chapter 6. Complete Bayes. Chapter 7. Naïve Bayes Classifier. Chapter 8. Stored Model for Naive Bayes Classifier. Chapter 9. Review of Mathematics for Neural Networks. Chapter 10. Calculus. Chapter 11. Neural Networks -- Feed Forward Process and Back Propagation Process. Chapter 12. Programming a Neural Network using OOP in R. Chapter 13. Adding in a Bias Term. Chapter 14. Modular Version of Neural Networks for Deep Learning. Chapter 15. Deep Learning with Convolutional Neural Networks. Chapter 16. R Packages for Neural Networks, Deep Learning, and Naïve Bayes.</P>
520 _aBased on the author's experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R: From the Ground Up reveals how machine learning algorithms do their magic and explains how these algorithms can be implemented in code. It is designed to provide readers with an understanding of the reasoning behind machine learning algorithms as well as how to program them. Written for novice programmers, the book progresses step-by-step, providing the coding skills needed to implement machine learning algorithms in R. The book begins with simple implementations and fundamental concepts of logic, sets, and probability before moving to the coverage of powerful deep learning algorithms. The first eight chapters deal with probability-based machine learning algorithms, and the last eight chapters deal with machine learning based on artificial neural networks. The first half of the book does not require mathematical sophistication, although familiarity with probability and statistics would be helpful. The second half assumes the reader is familiar with at least one semester of calculus. The text guides novice R programmers through algorithms and their application and along the way; the reader gains programming confidence in tackling advanced R programming challenges. Highlights of the book include: More than 400 exercises A strong emphasis on improving programming skills and guiding beginners to the implementation of full-fledged algorithms Coverage of fundamental computer and mathematical concepts including logic, sets, and probability In-depth explanations of machine learning algorithms
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aMachine learning.
650 0 _aR (Computer program language)
650 0 _aProgramming (Mathematics)
650 7 _aCOMPUTERS / Artificial Intelligence
_2bisacsh
650 7 _aCOMPUTERS / Programming / Algorithms
_2bisacsh
650 7 _aCOMPUTERS / Programming / Object Oriented
_2bisacsh
856 4 0 _3Read Online
_uhttps://www.taylorfrancis.com/books/9781003051220
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _2lcc
_cEBK
999 _c18817
_d18817