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Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
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Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package.
Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
Número do item: 112607187

Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024

Número do item: 112607187

R$ 493

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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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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.

Detalhes do produto

Compre Linear Algebra for Data Science, Machine Learning, and Signal online ao melhor preço no Ubuy Brasil. Obtenha ofertas exclusivas, descontos e opção de entrega rápida.
Item Weight2 lbs (910 grams)

Who Should Buy?

Suitable For
  • 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.

  • Signal Processing Engineers

    Engineers working in signal processing will appreciate the practical applications of linear algebra in their field.

Not Suitable For
  • Beginners in Mathematics

    Complete novices in mathematics may struggle with the complex concepts presented without prior foundational knowledge.

  • 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.

DESCRIÇÃO DO PRODUTO

Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024

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Perguntas e Respostas do cliente

  • pergunta: Who is this textbook intended for?

    responda: It is perfect for upper-level undergraduates and first-year graduate students with prior linear algebra knowledge.
  • pergunta: Does the book include exercises for students?

    responda: Yes, it includes over 200 multiple-choice questions and homework exercises with solutions.
  • pergunta: Are there practical applications included in the book?

    responda: Absolutely, it highlights various applications such as low-rank approximation, logistic regression, and dimensionality reduction.

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Prós

  • Comprehensive coverage of topics
  • Accessible to beginners
  • Clear explanations and examples
  • Useful for various applications
  • Well-structured and organized

Contras

  • Some advanced topics may require further study.

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R$ 493

Peça agora e receba por volta de Terça, Junho 30

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Características e benefícios

  • 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.