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Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
USD 79
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Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package.
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| Item Weight | 2 lbs (910 grams) |
Who Should Buy?
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Data Science Students
Students pursuing data science will benefit from foundational linear algebra skills necessary for data analysis and modeling.
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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.
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Non-Technical Readers
Readers without a technical background may find the content difficult to understand and apply in practical terms.
DESKRIPSI PRODUK
Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
Tanya & Jawab Pelanggan
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pertanyaan:
Who is this textbook intended for?
menjawab: It is perfect for upper-level undergraduates and first-year graduate students with prior linear algebra knowledge. -
pertanyaan:
Does the book include exercises for students?
menjawab: Yes, it includes over 200 multiple-choice questions and homework exercises with solutions. -
pertanyaan:
Are there practical applications included in the book?
menjawab: Absolutely, it highlights various applications such as low-rank approximation, logistic regression, and dimensionality reduction.
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Pro
- Comprehensive coverage of topics
- Accessible to beginners
- Clear explanations and examples
- Useful for various applications
- Well-structured and organized
Kontra
- Some advanced topics may require further study.
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USD 79
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Fitur & Manfaat
- 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.
