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Preces #:
112607187
Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
Preces #:
112607187
€ 72
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What Stands Out
Comprehensive Coverage
Offers a thorough exploration of linear algebra concepts, tailored specifically for applications in data science, machine learning, and signal processing, ensuring a deep understanding for learners.
Practical Applications
Focuses on real-world applications and problem-solving techniques, enabling readers to effectively apply linear algebra techniques in their projects and research across various emerging fields.
Expertly Crafted
Written by industry experts and academics, this resource provides a blend of theory and practice, equipping readers with the high-quality knowledge necessary to excel in modern analytical environments.
Produkta informācija
| Item Weight | 2 lbs (910 grams) |
Who Should Buy?
-
Data Science Students
Students pursuing data science will benefit from foundational linear algebra skills necessary for data analysis and modeling.
-
Machine Learning Practitioners
Practitioners looking to deepen their understanding of machine learning algorithms will find valuable insights in this text.
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Signal Processing Engineers
Engineers working in signal processing will appreciate the practical applications of linear algebra in their field.
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Beginners in Mathematics
Complete novices in mathematics may struggle with the complex concepts presented without prior foundational knowledge.
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Casual Learners
Individuals seeking a light introduction to linear algebra might find this textbook too dense and technical.
-
Non-Technical Readers
Readers without a technical background may find the content difficult to understand and apply in practical terms.
PRODUKTA APRAKSTS
Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
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Klientu jautājumi un atbildes
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jautājums:
Who is this textbook intended for?
atbildi: It is perfect for upper-level undergraduates and first-year graduate students with prior linear algebra knowledge. -
jautājums:
Does the book include exercises for students?
atbildi: Yes, it includes over 200 multiple-choice questions and homework exercises with solutions. -
jautājums:
Are there practical applications included in the book?
atbildi: Absolutely, it highlights various applications such as low-rank approximation, logistic regression, and dimensionality reduction.
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Pros
- Comprehensive coverage of topics
- Accessible to beginners
- Clear explanations and examples
- Useful for various applications
- Well-structured and organized
Mīnusi
- Some advanced topics may require further study.
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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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€ 72
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Funkcijas un priekšrocības
- Engaging textbook designed for upper-level undergraduates and first-year graduate students.
- Covers both basic and advanced matrix topics relevant to data science and machine learning.
- Includes 200+ multiple-choice questions for interactive classroom learning.
- Features practical exercises with solutions available for instructors.
- Encourages active learning through 'explore' questions and practical Julia code examples.
- Offers a suite of computational notebooks for hands-on learning experiences.