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About
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.
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About
Unlambda is a programming language. Nothing remarkable there. The originality of Unlambda is that it stands as the unexpected intersection of two marginal families of languages. Functional programming languages, of which the canonical representative is Scheme (a Lisp dialect). This means that the basic object manipulated by the language (and indeed the only one as far as Unlambda is concerned) is the function. Rather, Unlambda uses a functional approach to programming: the only form of objects it manipulates are functions. Each function takes a function as an argument and returns a function. Apart from a binary “apply” operation, Unlambda provides several built-in functions (the most important ones being the K and S combinators). User-defined functions can be created, but not saved or named, because Unlambda does not have any variables.
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About
Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.
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Platforms Supported
Windows
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Linux
Cloud
On-Premises
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Component Library solution for DevOps teams
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Audience
Developers in need of an advanced Programming Language solution
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Audience
Engineers and data scientists requiring a solution to manage and improve their machine learning research
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Support
Phone Support
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Online
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Support
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API
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Free
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Pricing
Free
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Free Trial
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Pricing
Free
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Free Trial
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Training
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Training
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Company InformationNumPy
numpy.org
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Company InformationUnlambda
www.madore.org/~david/programs/unlambda/
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Company Informationscikit-learn
United States
scikit-learn.org/stable/
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Integrations
3LC
Avanzai
Cython
DagsHub
Dash
Databricks Data Intelligence Platform
Flower
Gensim
Guild AI
Intel Tiber AI Studio
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Integrations
3LC
Avanzai
Cython
DagsHub
Dash
Databricks Data Intelligence Platform
Flower
Gensim
Guild AI
Intel Tiber AI Studio
|
Integrations
3LC
Avanzai
Cython
DagsHub
Dash
Databricks Data Intelligence Platform
Flower
Gensim
Guild AI
Intel Tiber AI Studio
|
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