Mastering numerical computing with NumPy : master the skills necessary for performing complex numerical computations and effective data manipula

Mastering numerical computing with NumPy master the skills necessary for performing complex numerical computations and effective data manipula

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Enhance the power of NumPy and start boosting your scientific computing capabilities About This Book Grasp all aspects of numerical computing and understand NumPy Explore examples to learn exploratory data analysis (EDA), regression, and clustering Access NumPy libraries and use performance benchmar...

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Detalles Bibliográficos
Otros Autores: Cuhadaroglu, Mert, author (author), Cakmak, Umit Mert, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham ; Mumbai : Packt 2018.
Edición:1st edition
Materias:
Cloud computing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630627006719
Tabla de Contenidos:
  • 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
  • Summary
  • Chapter 3: Exploratory Data Analysis of Boston Housing Data with NumPy Statistics
  • Loading and saving files
  • Exploring our dataset
  • Looking at basic statistics
  • Computing histograms
  • Explaining skewness and kurtosis
  • Trimmed statistics
  • Box plots
  • Computing correlations
  • Summary
  • Chapter 4: Predicting Housing Prices Using Linear Regression
  • Supervised learning and linear regression
  • Independent and dependent variables
  • Hyperparameters
  • Loss and error functions
  • Univariate linear regression with gradient descent
  • Using linear regression to model housing prices
  • Summary
  • Chapter 5: Clustering Clients of a Wholesale Distributor Using NumPy
  • Unsupervised 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 performance
  • Summary
  • 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 Benchmarks
  • Why 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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