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Mastering numerical computing with NumPy : master scientific computing and perform complex operations with ease /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Cakmak, Umit Mert, |e author. | |
| 245 | 1 | 0 | |a Mastering numerical computing with NumPy : |b master scientific computing and perform complex operations with ease / |c Umit Mert Cakmak, Mert Cuhadaroglu. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2018. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 338 | |a online resource |b cr |2 rdacarrier | ||
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| 588 | 0 | |a Online resource; title from title page (Safari, viewed July 27, 2018). | |
| 520 | 8 | |a Annotation |b Enhance the power of NumPy and start boosting your scientific computing capabilitiesKey FeaturesGrasp all aspects of numerical computing and understand NumPyExplore examples to learn exploratory data analysis (EDA), regression, and clusteringAccess NumPy libraries and use performance benchmarking to select the right toolBook DescriptionNumPy 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. What you will learn Perform vector and matrix operations using NumPy Perform exploratory data analysis (EDA) on US housing data Develop a predictive model using simple and multiple linear regression Understand unsupervised learning and clustering algorithms with practical use cases Write better NumPy code and implement the algorithms from scratch Perform benchmark tests to choose the best configuration for your systemWho this book is forMastering Numerical Computing with NumPy is for you if you are a Python programmer, data analyst, data engineer, or a data science enthusiast, who wants to master the intricacies of NumPy and build solutions for your numeric and scientific computational problems. You are expected to have familiarity with mathematics to get the most out of this book. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Numerical analysis |x Data processing. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 6 | |a Analyse numérique |x Informatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Numerical analysis |x Data processing |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 700 | 1 | |a Cuhadaroglu, Mert, |e author. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781788993357/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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