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Preces #:
135438898
World Standard MIT Textbook Strang: Linear Algebra and Data Science Tankobon Hardcover – October 28, 2021
Preces #:
135438898
€ 67
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A linear algebra textbook essential to the information age that data scientists should know!
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What Stands Out
Comprehensive Coverage
This textbook offers an extensive exploration of linear algebra principles tailored for data science applications, ensuring readers gain a solid foundation and practical insights.
MIT Quality
Authored by renowned educator Gilbert Strang, this book reflects MIT's high academic standards, providing users with top-tier educational content recognized globally.
Hardcover Durability
The tankobon hardcover format ensures durability for frequent use, making it an ideal investment for students and professionals seeking a reliable reference for linear algebra in data science.
Produkta informācija
| Publisher | u8fd1u4ee3u79d1u5b66u793e (October 28, 2021) |
| Publication date | October 28, 2021 |
| Language | Japanese |
| Tankobon Hardcover | 488 pages |
| ISBN-10 | 4764906007 |
| ISBN-13 | 978-4764906006 |
| Dimensions | 10.12 x 7.17 x 0.79 inches (25.7 x 18.2 x 2 cm) |
Who Should Buy?
-
Students of Mathematics
Ideal for undergraduate or graduate students studying linear algebra and its applications in data science.
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Data Science Professionals
Beneficial for practitioners seeking to deepen their understanding of linear algebra in data analysis and machine learning.
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Self-learners
Great for individuals looking to advance their knowledge independently in linear algebra and its relevance to data science.
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Beginners in Math
Not suitable for those without a basic understanding of mathematical concepts, as it may be too advanced.
-
Casual Readers
Individuals looking for light reading will find the textbook's technical nature unengaging and challenging.
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Professionals Outside Discipline
Those working in fields unrelated to data science or mathematics may not benefit from the specialized content.
PRODUKTA APRAKSTS
World Standard MIT Textbook Strang: Linear Algebra and Data Science Tankobon Hardcover – October 28, 2021
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Klientu jautājumi un atbildes
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jautājums:
Is this book suitable for beginners in data science?
atbildi: This book is designed as an advanced textbook, ideal for those with a foundational understanding of linear algebra. -
jautājums:
What mathematical concepts does this book cover?
atbildi: It covers linear algebra, probability statistics, and optimization, key components for data science. -
jautājums:
Why is understanding linear algebra important for data scientists?
atbildi: Linear algebra provides the mathematical foundation necessary for understanding neural networks and deep learning.
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Pros
- Engaging and informative content
- Well-structured lessons
- Great for beginners
- Hands-on exercises included
- Expert insights provided
Mīnusi
- Some concepts may be slightly complex.
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Svarīga informācija
- Ierobežojumi: attiecībā uz produktiem, kas tiek nosūtīti starptautiski, lūdzu, ņemiet vērā, ka jebkura ražotāja garantija var nebūt derīga; ražotāja servisa iespējas var nebūt pieejamas; produktu rokasgrāmatas, instrukcijas un drošības brīdinājumi var nebūt galamērķa valsts valodās; produkti (un pavadošie materiāli) var nebūt izstrādāti saskaņā ar galamērķa valsts standartiem, specifikācijām un marķēšanas prasībām; un produkti var neatbilst galamērķa valsts spriegumam un citiem elektriskajiem standartiem (ja nepieciešams, nepieciešams adapteris vai pārveidotājs). Par to, lai produktu varētu likumīgi ievest galamērķa valstī, ir atbildīgs saņēmējs. Pasūtot no Ubuy vai tā saistītajiem uzņēmumiem, saņēmējs ir ierakstu importētājs, un viņam jāievēro visi galamērķa valsts tiesību akti un noteikumi.
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€ 67
Pasūtiet tūlīt un saņemiet to Sestdiena, jūnijs 27
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Funkcijas un priekšrocības
- Essential reading for aspiring data scientists.
- Covers fundamental mathematics including linear algebra, probability statistics, and optimization.
- Written by MIT Professor Gilbert Strang, a noted expert in the field.
- Deep understanding of linear algebra is crucial for grasping neural networks and deep learning.
- Advanced textbook suitable for students and professionals alike.
- Few resources cover the underlying mathematics of deep learning like this book.