Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. Python backend system that decouples API from implementation unumpy provides a NumPy API. The City of Fawn Creek is located in the State of Kansas. It is our hope that after having worked through this primer, the student will feel con dent consulting such a manual. Manipulate JSON-like data with NumPy-like idioms. (The 3rd edition is called \Fortran 90/2003 explained') in which most Fortran statements are de ned and examples are given. Simply Fortran now supports the automatic renaming of Fortran programming elements, including functions, subroutines, modules, common block variables, and local variables. Our customers, both current and potential, are treated personalized support with rapid turnaround times via email and community forums. Early IBM computers did not support lower case letters and the names of versions of the language through FORTRAN 77 were usually spelled in all-uppercase (. Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.ĭeep learning framework that accelerates the path from research prototyping to production deployment.Īn end-to-end platform for machine learning to easily build and deploy ML powered applications.ĭeep learning framework suited for flexible research prototyping and production.Ī cross-language development platform for columnar in-memory data and analytics. Labeled, indexed multi-dimensional arrays for advanced analytics and visualization NumPy-compatible array library for GPU-accelerated computing with Python.Ĭomposable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU. NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides.ĭistributed arrays and advanced parallelism for analytics, enabling performance at scale. With this power comes simplicity: a solution in NumPy is often clear and elegant. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. Nearly every scientist working in Python draws on the power of NumPy.
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