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Mastering Numerical Computing with NumPy : Master Scientific Computing and Perform Complex Operations with Ease.
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: Predict...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
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.
- 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.