Numerical Python: Scientific Computing and Data Science Applications with. Numpy For more information, reference our Print and eBook Bulk Sales extracted using either “File ➤ Download as ➤ Python” or the Jupyter utility nbconvert Mastering these techniques is an important skill of a computational scientist. Mastering Numerical Computing with NumPy, published by Packt We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it. Mastering numerical computing with numpy 1st pdf enhance the power of Free download a newbies guide to the nexus 4: everything you need to know.
Mastering Numerical Computing with NumPy : Master Scientific Computing and Perform Complex Operations with Ease.
| Genre/Form: | Electronic books |
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| Additional Physical Format: | Print version:
Cuhadaroglu, Mert.
Mastering Numerical Computing with NumPy : Master Scientific Computing and Perform Complex Operations with Ease.
Birmingham : Packt Publishing Ltd, ©2018 |
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| Material Type: | Document, Internet resource |
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| Document Type: | Internet Resource, Computer File |
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| All Authors / Contributors: | Mert Cuhadaroglu; Umit Mert Cakmak |
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| ISBN: | 9781788996846 1788996844 9781788993357 1788993357 |
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| OCLC Number: | 1043647876 |
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| Description: | 1 online resource (237 pages) |
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| Contents: | Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Working with NumPy Arrays; Technical requirements; Why do we need NumPy?; Who uses NumPy?; Introduction to vectors and matrices; Basics of NumPy array objects; NumPy array operations; Working with multidimensional arrays; Indexing, slicing, reshaping, resizing, and broadcasting; Summary; Chapter 2: Linear Algebra with NumPy; Vector and matrix mathematics ; What's an eigenvalue and how do we compute it?; Computing the norm and determinant; Solving linear equations; Computing gradient. Chapter 5: Clustering Clients of a Wholesale Distributor Using NumPyUnsupervised learning and clustering; Hyperparameters; The loss function; Implementing our algorithm for a single variable; Modifying our algorithm; Summary; Chapter 6: NumPy, SciPy, Pandas, and Scikit-Learn; NumPy and SciPy; Linear regression with SciPy and NumPy; NumPy and pandas; Quantitative modeling with stock prices using pandas; SciPy and scikit-learn; K-means clustering in housing data with scikit-learn; Summary; Chapter 7: Advanced Numpy; NumPy internals; How does NumPy manage memory? Profiling NumPy code to understand the performanceSummary; Chapter 8: Overview of High-Performance Numerical Computing Libraries; BLAS and LAPACK; ATLAS; Intel Math Kernel Library; OpenBLAS; Configuring NumPy with low-level libraries using AWS EC2; Installing BLAS and LAPACK; Installing OpenBLAS; Installing Intel MKL; Installing ATLAS; Compute-intensive tasks for benchmarking; Matrix decomposition; Singular-value decomposition; Cholesky decomposition; Lower-upper decomposition; Eigenvalue decomposition; QR decomposition; Working with sparse linear systems; Summary. Chapter 9: Performance BenchmarksWhy do we need a benchmark?; Preparing for a performance benchmark; Performance with BLAS and LAPACK; Performance with OpenBLAS; Performance with ATLAS; Performance with Intel MKL; Results; Summary; Other Books You May Enjoy; Index. |
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