| 000 | 02606nam a22002177a 4500 | ||
|---|---|---|---|
| 999 |
_c2479 _d2479 |
||
| 003 | OSt | ||
| 005 | 20200224142453.0 | ||
| 008 | 200118b ||||| |||| 00| 0 eng d | ||
| 020 | _a978-1-107-51282-5 | ||
| 028 |
_bAllied Informatics, Jaipur _c7084 _d13/01/2020 _q2019-20 |
||
| 040 |
_aBSDU _bEnglish _cBSDU |
||
| 082 |
_a006.31 _bSHA |
||
| 100 | _aShalev-Shwartz,Shai | ||
| 245 | _aUnderstanding Machine Learning:From theory to algorithms | ||
| 260 |
_aNew Delhi _bCambridge University Press _c2020 |
||
| 300 | _a397 | ||
| 504 | _aDescriptionContentsResourcesCoursesAbout the Authors Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering. This book is written for the first year students of Engineering– A blend of theory and solved problems will equip the students with the fundamental knowledge and application of the coding concepts. This will nurture them to have a strong foundation for the courses in the subsequent semesters. This book ensures a smooth and successful transition to being a skilled Python expert. The book uses a simple-to-complex and easy-to-learn approach throughout. The concept of ‘learning by-solving has been stressed everywhere in the book. Each feature of Python is treated in depth followed by a complete program example to illustrate its use. Wherever necessary, concepts are explained pictorially to facilitate better understanding. The book presents a contemporary approach to programming, offering a combination of theory and practice. | ||
| 650 | _aMachine Learning | ||
| 700 | _aBen-David,Shai | ||
| 942 |
_2ddc _cBK |
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