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- Apache MXNet | A flexible and efficient library for deep learning.
APACHE MXNET: A FLEXIBLE AND EFFICIENT LIBRARY FOR DEEP LEARNING A truly open source deep learning framework suited for flexible research prototyping and production
- DL框架之MXNet ：深度学习框架之MXNet 的简介、安装、使用方法、应用案例之详细攻略 - 知乎
MXnet是一个多语言机器学习（ML）库，用于简化ML算法的开发，特别是对于深度神经网络。 它嵌入在宿主语言中，将声明性符号表达式与命令式张量计算混合在一起。 它提供自动微分来推导梯度。 MXnet具有计算和内存效率高的特点，可以在各种异构系统上运行，从移动设备到分布式GPU集群。 本文介绍了MXnet的API设计和系统实现，并解释了如何统一处理符号表达式和张量操作的嵌入。
- mxnet·PyPI
Apache MXNet is a deep learning framework designed for both efficiency and flexibility It allows you to mix the flavours of deep learning programs together to maximize the efficiency and your productivity For feature requests on the PyPI package, suggestions, and issue reports, create an issue by clicking here Prerequisites
- What Is Apache MXNet? - Apache MXNet on AWS
Apache MXNet (MXNet) is an open source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of platforms, from cloud infrastructure to mobile devices
- What Is Apache MXNet and Why Does It Matter? - NVIDIA
Apache MXNet is a flexible and scalable deep learning framework that supports many deep learning models and languages It’s a flexible, general-purpose framework with popular use cases ranging from mobile apps to autonomous vehicles
- Apache MXNet: Architecture, Distributed Training, and Deployment
Explore Apache MXNet in depth—from its modular architecture and distributed training features to practical deployment strategies in the cloud
- Get Started - Apache MXNet
Please indicate your preferred configuration below to see specific instructions A flexible and efficient library for deep learning
- Apache MXNet for Deep Learning - GitHub
Apache 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
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