Mastering Numerical Computing with NumPy - Info and Reading Options
Master Scientific Computing and Perform Complex Operations with Ease
By Umit Mert Cakmak and Mert Cuhadaroglu
"Mastering Numerical Computing with NumPy" was published by de Gruyter GmbH, Walter in 2018 - Erscheinungsort nicht ermittelbar, it has 248 pages and the language of the book is English.
“Mastering Numerical Computing with NumPy” Metadata:
- Title: ➤ Mastering Numerical Computing with NumPy
- Authors: Umit Mert CakmakMert Cuhadaroglu
- Language: English
- Number of Pages: 248
- Publisher: de Gruyter GmbH, Walter
- Publish Date: 2018
- Publish Location: ➤ Erscheinungsort nicht ermittelbar
“Mastering Numerical Computing with NumPy” Subjects and Themes:
- Subjects: ➤ NumPy - Numerical Computing - Numerical analysis, data processing - Python (computer program language)
Edition Identifiers:
- The Open Library ID: OL53994519M - OL19542654W
- ISBN-13: 9781788996846 - 9781788993357
- All ISBNs: 9781788996846 - 9781788993357
AI-generated Review of “Mastering Numerical Computing with NumPy”:
"Mastering Numerical Computing with NumPy" Description:
Open Data:
NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts. Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system. By the end of this book, you will have become an expert in handling and performing complex data manipulations
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