![]() ![]() ![]() 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. Manipulate JSON-like data with NumPy-like idioms. 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. The Laerdal-SonoSim Procedure Trainer upgrade includes all required items except for the Laerdal-SonoSim PC and Laerdal-SonoSim Probe. Labeled, indexed multi-dimensional arrays for advanced analytics and visualization SonoSim Ultrasound Solution (LSUS) customers to upgrade their existing LSUS PC to include the Laerdal-SonoSim Procedure Trainer software and 5 year license and, in addition, use their existing Laerdal-SonoSim Probe. 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. Can't find what you're looking for We can help. With this power comes simplicity: a solution in NumPy is often clear and elegant. Product Downloads Product Downloads Product Downloads Software - Demos, Full versions and Updates (in EXE & ZIP formats) Directions For Use (PDF) Other product documentation (PDF) Find your product below to see all available downloads. 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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