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Apple Open-sources Apple Silicon-Optimized Machine Learning Framework MLX
Apple's MLX combines familiar APIs, composable function transformations, and lazy computation to create a machine learning framework inspired by NumPy and PyTorch that is optimized for Apple Silicon. Implemented in Python and C++, the framework aims to provide a user-friendly and efficient solution to train and deploy machine learning models on Apple Silicon.
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Apache Spark Brings Pandas API with Version 3.2
The Apache Spark team has integrated the Pandas API in the product's latest 3.2 release. With this change, dataframe processing can be scaled to multiple clusters or multiple processors in a single machine using the PySpark execution engine.
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NumPy 1.20 Released with Runtime SIMD Support and Type Annotations
NumPy 1.20 was recently released with new features focusing on performance and documentation. Developers can now use type annotations for NumPy functions. A wider use of SIMD (Single Instruction, Multiple Data) instructions increases the execution speed of universal functions (ufunc). NumPy’s documentation additionally sees significant improvements.
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AFK-MC² Algorithm Speeds up k-Means Clustering Algorithm Seeding
“Fast and Probably Good Seedings for k-Means” by Olivier Bachem et al. was presented on 2016’s Neural Information Processing Systems (NIPS) conference and describes AFK-MC2, an alternative method to generate initial seedings for k-Means clustering algorithm that is several orders of magnitude faster than the state of art method k-Means++.
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NumPy and SciPy for .NET
As part of the Python Tools for Visual Studio project the well-known NumPy and SciPy libraries were ported to .NET. The port, which combines C# and C interfaces over a native C core, was done in such a way that all .NET languages can take advantage of it.