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ODBIERZ TWÓJ BONUS :: »

Practical Linear Algebra for Data Science

(ebook) (audiobook) (audiobook) Książka w języku 1
Autor:
Mike X Cohen
Practical Linear Algebra for Data Science Mike X Cohen - okladka książki

Practical Linear Algebra for Data Science Mike X Cohen - okladka książki

Practical Linear Algebra for Data Science Mike X Cohen - audiobook MP3

Practical Linear Algebra for Data Science Mike X Cohen - audiobook CD

Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
328
Dostępne formaty:
     ePub
     Mobi

Ebook (245,65 zł najniższa cena z 30 dni)

299,00 zł (-15%)
254,15 zł

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(245,65 zł najniższa cena z 30 dni)

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Do przechowalni

If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications.

This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms.

Ideal for practitioners and students using computer technology and algorithms, this book introduces you to:

  • The interpretations and applications of vectors and matrices
  • Matrix arithmetic (various multiplications and transformations)
  • Independence, rank, and inverses
  • Important decompositions used in applied linear algebra (including LU and QR)
  • Eigendecomposition and singular value decomposition
  • Applications including least-squares model fitting and principal components analysis

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O autorze książki

Mike X Cohen is an associate professor at the Radboud University Medical Center and the leader of the Synchronization in the Neural Systems research group. His research focuses on using state-of-the-art neuroscience methods to understand the mechanisms and implications of brain circuit dynamics and has been funded by government agencies in the US, Germany, Netherlands, and Europe, and by private institutions and medical centers.



Mike has been teaching time series analysis, applied mathematics, and scientific programming for almost 20 years. He has published several textbooks on these topics and teaches a variety of real-life and online courses.

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