Optimization for Data Analysis
Product ID: 456777455
📊Focus on data science & machine learning algorithms
📘300 pages of core optimization knowledge
⚙️Covers gradient & stochastic gradient methods
€ 117.53
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Description
📈 Unlock the math behind data mastery — don’t get left behind!
- COMPACT YET DENSE - 300 pages of tightly packed, essential optimization theory
- DATA SCIENCE FOCUSED - Tailored content for practical optimization in analytics
- HARDCOVER DURABILITY - A professional, long-lasting reference for your bookshelf
- COMPREHENSIVE ALGORITHM COVERAGE - From gradient descent to back-propagation, all in one place
- CORE OPTIMIZATION TECHNIQUES UNPACKED - Master the algorithms powering data science and ML
Optimization for Data Analysis by Stephen J. Wright is a 300-page hardcover book that delves into fundamental optimization algorithms crucial for data science and machine learning. It covers key methods such as gradient descent, stochastic gradient, coordinate descent, and back-propagation, providing a compact, theory-focused resource aimed at students and practitioners seeking a deep understanding of optimization techniques relevant to data analysis.
Specifications
| Customer reviews | 3.8 3.8 out of 5 stars (18) |
| Dimensions | 15.88 x 1.91 x 23.5 cm |
| Edition | New |
| ISBN-10 | 1316518981 |
| ISBN-13 | 978-1316518984 |
| Item weight | 450 g |
| Language | English |
| Print length | 238 pages |
| Publication date | 21 April 2022 |
| Publisher | Cambridge University Press |
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