机读格式显示(MARC)

• 000 03609cam a2200361 i 4500

• 001 0000825902

• 003 OCoLC

• 005 20210622111600.0

• 008 200818t20202021flua b 001 0 eng d

• 020 __ |a 9780367561949 : |c CNY822.51

• 020 __ |a 0367561948

• 020 __ |a 9780367507855

• 020 __ |a 0367507854

• 040 __ |a UKMGB |b eng |c UKMGB |e rda |d OCLCO |d OCLCF |d ZAQ |d YDX |d GZN |d FIE

• 050 _4 |a Q325.5 |b .C53 2020

• 090 __ |a TP181/BC1

• 093 __ |a TP181 |2 5

• 100 1_ |a Claster, William B., |e author.

• 245 10 |a Mathematics and programming for machine learning with R : |b from the ground up / |c William B. Claster.

• 250 __ |a First edition.

• 260 __ |a Boca Raton, FL : |b CRC Press, Taylor & Francis Group, |c 2020.

• 300 __ |a xxi, 408 pages : |b illustrations ; |c 26 cm

• 336 __ |a text |b txt |2 rdacontent

• 336 __ |a still image |2 rdacontent

• 337 __ |a unmediated |b n |2 rdamedia

• 338 __ |a volume |b nc |2 rdacarrier

• 504 __ |a Includes bibliographical references and index.

• 505 0_ |a Chapter 1. Functions Tutorial. -- Chapter 2. Logic and R. -- Chapter 3. Sets with R: Building the Tools. -- Chapter 4. Probability. -- Chapter 5. Naiì?ve Rule. -- Chapter 6. Complete Bayes. -- Chapter 7. Naiì?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 Naiì?ve Bayes.

• 520 __ |a Based 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. -- |c Provided by publisher.

• 650 _0 |a Machine learning.

• 650 _0 |a R (Computer program language)

• 650 _0 |a Programming (Mathematics)

• 920 __ |a 233030 |z 1