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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 |
|---|---|
| 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 |
MARC
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| 100 | 1 | |a Cuhadaroglu, Mert. | |
| 245 | 1 | 0 | |a Mastering Numerical Computing with NumPy : |b Master Scientific Computing and Perform Complex Operations with Ease. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (237 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 505 | 8 | |a 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? | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a Mastering Numerical Computing with Python guides you in performing complex computing with cutting-edge coverage on advanced concepts such as exploratory data analysis and clustering algorithms. You'll become an expert in addressing matrix calculations, and write efficient NumPy codes for implementing algorithms with real-world examples. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Cloud computing. | |
| 650 | 6 | |a Infonuagique. | |
| 650 | 7 | |a Data capture & analysis. |2 bicssc | |
| 650 | 7 | |a Data mining. |2 bicssc | |
| 650 | 7 | |a Programming & scripting languages: general. |2 bicssc | |
| 650 | 7 | |a Computers |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Database Management |x Data Mining. |2 bisacsh | |
| 650 | 7 | |a Computers |x Programming Languages |x Python. |2 bisacsh | |
| 650 | 7 | |a Cloud computing |2 fast | |
| 700 | 1 | |a Mert Cakmak, Umit. | |
| 776 | 0 | 8 | |i Print version: |a Cuhadaroglu, Mert. |t Mastering Numerical Computing with NumPy : Master Scientific Computing and Perform Complex Operations with Ease. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781788993357 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5439796 |z Texto completo |
| 938 | |a EBL - Ebook Library |b EBLB |n EBL5439796 | ||
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