DETAIL BUKU
  • Learning scipy for numerical and scientific computing

    Learning scipy for numerical and scientific computing

    • Sergio J Rojas G
    • Packt Publishing Ltd
    • 2015
    • 978-1-78398-770-2
    Sinopsis

    SciPy is an open source Python library used to perform scientific computing. The SciPy (Scientific Python) package extends the functionality of NumPy with a substantial collection of useful algorithms. The book starts with a brief description of the SciPy libraries, followed by a chapter that is a fun and fast-paced primer on array creation, manipulation, and problem-solving. You will also learn how to use SciPy in linear algebra, which includes topics such as computation of eigenvalues and eigenvectors. Furthermore, the book is based on interesting subjects such as definition and manipulation of functions, computation of derivatives, integration, interpolation, and regression. You will also learn how to use SciPy in signal processing and how applications of SciPy can be used to collect, organize, analyze, and interpret data. By the end of the book, you will have fast, accurate, and easy-to-code solutions for numerical and scientific computing applications.

    Kata Kunci
    Tersedia di Perpustakaan Kampus:
    • Tasikmalaya, BSD
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Kode Buku : 240453
Kode Klasifikasi : 518
Judul Buku : Learning scipy for numerical and scientific computing
Edisi : 2
Penulis : Sergio J Rojas G
Penerbit : Packt Publishing Ltd
Bahasa : Inggris
Tahun : 2015
ISBN : 978-1-78398-770-2
Tajuk Subjek : Numerik
Deskripsi : 251 hal, 30 cm
Eksemplar : 2
Stok : 2
Petugas : Linda Puji Astuti
SciPy is an open source Python library used to perform scientific computing. The SciPy (Scientific Python) package extends the functionality of NumPy with a substantial collection of useful algorithms.

The book starts with a brief description of the SciPy libraries, followed by a chapter that is a fun and fast-paced primer on array creation, manipulation, and problem-solving. You will also learn how to use SciPy in linear algebra, which includes topics such as computation of eigenvalues and eigenvectors. Furthermore, the book is based on interesting subjects such as definition and manipulation of functions, computation of derivatives, integration, interpolation, and regression. You will also learn how to use SciPy in signal processing and how applications of SciPy can be used to collect, organize, analyze, and interpret data.

By the end of the book, you will have fast, accurate, and easy-to-code solutions for numerical and scientific computing applications.
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