國立虎尾科技大學 |

Applied machine learning

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Applied machine learning/ by David Forsyth.
作者:	Forsyth, David.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xxi, 494 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-030-18114-7
ISBN:	9783030181147

Applied machine learning
Forsyth, David.

Applied machine learning[electronic resource] /by David Forsyth. - Cham :Springer International Publishing :2019. - xxi, 494 p. :ill., digital ;24 cm.

1. Learning to Classify -- 2. SVM's and Random Forests -- 3. A Little Learning Theory -- 4. High-dimensional Data -- 5. Principal Component Analysis -- 6. Low Rank Approximations -- 7. Canonical Correlation Analysis -- 8. Clustering -- 9. Clustering using Probability Models -- 10. Regression -- 11. Regression: Choosing and Managing Models -- 12. Boosting -- 13. Hidden Markov Models -- 14. Learning Sequence Models Discriminatively -- 15. Mean Field Inference -- 16. Simple Neural Networks -- 17. Simple Image Classiﬁers -- 18. Classifying Images and Detecting Objects -- 19. Small Codes for Big Signals -- Index.

Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one's own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use) Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career. Broad coverage of the area ensures enough to get the reader started, and to realize that it's worth knowing more in-depth knowledge of the topic. Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material.

ISBN: 9783030181147

Standard No.: 10.1007/978-3-030-18114-7doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5 / .F67 2019

Dewey Class. No.: 006.31

Applied machine learning
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520 $a Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one's own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use) Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career. Broad coverage of the area ensures enough to get the reader started, and to realize that it's worth knowing more in-depth knowledge of the topic. Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material.
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