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66264055
The Hundred-Page Machine Learning Book (The Hundred-Page Books)
товар №:
66264055
KZT 20042
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What Stands Out
Concise Learning
This book distills complex machine learning concepts into a concise format, making it accessible for beginners and efficient for seasoned professionals seeking a quick reference.
Broad Coverage
Covers a wide range of machine learning topics, from fundamental algorithms to advanced techniques, providing a comprehensive overview that caters to diverse learning needs.
Durable Edition
The hard cover ensures longevity and durability, making it a perfect addition to any bookshelf, while its compact size allows for easy portability for on-the-go learning.
Информация о продукте
| Publisher | Andriy Burkov |
| Publication date | January 13, 2019 |
| Language | English |
| Print length | 160 pages |
| ISBN-10 | 1777005477 |
| ISBN-13 | 978-1777005474 |
| Item Weight | 3.53 ounces (100.08 grams) |
| Dimensions | 7.5 x 0.38 x 9.25 inches (19.1 x 1 x 23.5 cm) |
| Part of series | The Hundred-Page Books |
Who Should Buy?
-
Beginners in ML
This book offers a concise introduction to machine learning concepts, making it ideal for those just starting their journey.
-
Self-learners
Individuals who prefer independent study will find this book's structured format easy to follow and understand.
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Students
University students studying computer science or related fields can benefit from this book as a supplementary resource.
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Advanced Practitioners
Experienced machine learning professionals may find the content too basic and lacking in depth for advanced applications.
ОПИСАНИЕ ТОВАРА
The Hundred-Page Machine Learning Book (The Hundred-Page Books)
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Intelligence & Semantics Editorial Review
The Hundred-Page Machine Learning Book is a fantastic read for someone who wants to quickly understand machine learning. This book has every important model and provides brief details for each one. It is clear, concise and excellent for quick to the point descriptions of mathematical implementations of machine learning calculations. Though there are moments when it gets bogged down in minutiae or dives into difficult-to-follow math at the start of a chapter, the content is still good. The section on transfer learning stands out. The book provides a summation of the current primary approaches to machine learning, and anyone familiar with the topic will enjoy its brevity, whereas if you are not, you'll enjoy its scope and early explanations that provide excellent context for the follow-on chapters. Overall, this book is highly recommended.
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Плюсы
- Contains every important machine learning model.
- Excellent material for quick references
- Clear and concise
- Excellent for quick to the point descriptions of mathematical implementations of machine learning calculations
- Covers important topics with enough depth but not so technical where you get lost.
- Provides a summation of the current primary approaches to machine learning.
Минусы
- Cannot be read on the Kindle Paperwhite.
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