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
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📈 Unlock the math behind data mastery — don’t get left behind!
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.
| Best Sellers Rank | #430,528 in Books ( See Top 100 in Books ) #959 in Databases & Big Data #38,624 in Reference |
| Customer Reviews | 3.8 out of 5 stars 17 Reviews |
S**R
poorly written & explained
very poorly written & explained.. sections of it are completely incomprehensible ...you cannot easily self-study this book
E**S
Terrible for people who are new to the field of optimization
While this book contains a collection of fundamental results in the field, it is difficult to recommend this book to learn the subject. The text lacks clarity, and seems to move from one result to the next without much explanation. The proofs are hard to follow, often omitting important steps, and, to a reader who is unfamiliar with the field, seemingly draw conclusions from thin air. In addition, authors often use unfamiliar concepts and notation without explaining their meaning, sometimes making the text impenetrable to people who are new to optimization theory.
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