LEADER 03760oam 22004811 450 001 9910794321703321 005 20210104012401.0 010 $a1-00-305122-7 010 $a1-000-19699-2 010 $a1-003-05122-7 010 $a1-000-19697-6 024 7 $a10.1201/9781003051220 035 $a(CKB)4100000011458747 035 $a(MiAaPQ)EBC6352549 035 $a(OCoLC)1198598560 035 $a(OCoLC-P)1198598560 035 $a(FlBoTFG)9781003051220 035 $a(EXLCZ)994100000011458747 100 $a20200818h20202021 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMathematics and R programming for machine learning $efrom the ground up /$fWilliam B. Claster 210 1$aBoca Raton, FL :$cCRC Press,$d2020. 210 4$dİ2021 215 $a1 online resource (431 pages) 300 $aIncludes index. 311 $a0-367-50785-4 311 $a0-367-56194-8 327 $a

Chapter 1. Functions Tutorial. Chapter 2. Logic and R. Chapter 3. Sets with R: Building the Tools. Chapter 4. Probability. Chapter 5. Nai?ve Rule. Chapter 6. Complete Bayes. Chapter 7. Nai?ve 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 Nai?ve Bayes.

330 $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 606 $aR (Computer program language) 615 0$aR (Computer program language) 676 $a519.502855133 700 $aClaster$b William B.$01585205 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910794321703321 996 $aMathematics and R programming for machine learning$93869583 997 $aUNINA