MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.
Looking for other MXNet-like programs? We've rounded up our favorites MXNet alternatives. Try any one of these fan favorites to see if they'll support your system and meet your specific needs.
TensorFlow is an open source software library for machine learning in various kinds of perceptual and language understanding tasks. It was originally developed by the...
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Open source deep learning platform that provides a seamless path from research prototyping to production deployment.
CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R.
Darknet: Open Source Neural Networks in C Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and...
mlpack is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of...
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Enterprise scale, open-source, distributed deep-learning library written for Java and Scala.
PyCaret is an open source low-code machine learning library in Python that aims to reduce the hypothesis to insights cycle time in a ML experiment. It enables data...
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The Microsoft Cognitive Toolkit - CNTK - is a unified deep-learning toolkit by Microsoft Research.
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