Apache MXNet

Apache MXNet (incubating) 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.

Editors
Thomas Delteil

Applied Scientist @ AWS - Deep Engine https://github.com/ThomasDelteil

Thom Lane

Machine Learning Scientist for AWS AI

Aaron Markham

Technologist and troublemaker. Programmer writer at Amazon AI and contributor to MXNet (http://mxnet.io).

Soji Adeshina

Software Engineer at AWS Deep Learning

Latest Posts

How Cimpress Delivers Cloud Inference for its Image Processing Services

This article discusses the problems our team at Cimpress™ had developing cloud inference services and how MXNet Model Server helped solve…

Quantizing Neural Network Models in MXNet for Strict Consistency on Blockchain

Towards A Novel Deterministic Inference Infrastructure on Blockchain

Model Quantization for Production-Level Neural Network Inference

Author: Patric Zhao, Xinyu Chen, Zhennan Qin, Jason Ye

Chainer model inference in Java, with ONNX and Apache MXNet

Authors: Vandana Kannan, Roshani Nagmote

The Learning Robot

Humans and machines, hand in hand

GluonNLP 0.6: Closing the Gap in Reproducible Research with BERT

GluonNLP is a deep learning toolkit for natural language processing. We are releasing GluonNLP v0.6 with pre-training scripts for BERT.

Machine translation from scratch with MXNet and R

In this post, we’ll see how to develop a complete machine translation system from scratch using the MXNet R package. We’ll achieve a BLEU…

Distributed Training using Apache MXNet with Horovod

Distributed training on multiple GPU instances can reduce the time to train modern deep neural networks on large data from weeks to…

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