AI News, Apache MXNet (incubating) for Deep Learning

Apache MXNet (incubating) for Deep Learning

Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It

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

MXNet combines aspects of each of these projects to achieve flexibility, speed, and memory efficiency.

Apache MXNet (incubating) for Deep Learning

Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It

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

MXNet combines aspects of each of these projects to achieve flexibility, speed, and memory efficiency.

Projects

As a large-scale machine learning researcher, I like to build real things that can be used in production. I

It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends.

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.

XGBoost provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.

Flink and Spark) Backgroun Story: I created XGBoost when doing research related to variants of tree boosting and cannot find a fast tree boosting package for my experiments.

In its core, a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.

This is a truely collaborative project that combines the wisdom from several deep learning projects and researchers from many different inistitute.

MShadow is an efficient, device invariant and simple tensor library for machine learning project that aims for both simplicity and performance. Basically,

we build this libary, with the power of template programming of C++, we are finally able to write weight += learning_rate * gradient :) When you write distributed machine learning.

light weight library that provides a fault tolerant interface of Allreduce and Broadcast for portable , scalable and reliable distributed machine learning programs.

project provides an abstract framework to build new matrix factorization variants simply by defining features. This

Deep Dive into Deep Learning with MXNet by Randall Hunt

Deep Learning is a serious discipline that is taking over many of the software development problems, and taking them from the cold world of data centers and ...

Lecture 8 | Deep Learning Software

In Lecture 8 we discuss the use of different software packages for deep learning, focusing on TensorFlow and PyTorch. We also discuss some differences ...

PyTorch in 5 Minutes

I'll explain PyTorch's key features and compare it to the current most popular deep learning framework in the world (Tensorflow). We'll then write out a short ...

MIT 6.S094: Introduction to Deep Learning and Self-Driving Cars

This is lecture 1 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. Course website: Lecture 1 slides: ..

CppCon 2017: Peter Goldsborough “A Tour of Deep Learning With C++”

— Presentation Slides, PDFs, Source Code and other presenter materials are available at: — Deep .

Machine Learning on the Cloud with MXNet

On July 19, 2017, AWS' Angel Pizarro presented "Machine Learning on the Cloud with MXNet" to the CBIIT Speaker Series.

Deep Learning Demystified w/ @AnimaAnandkumar #DataTalk

Every week, we talk about important data science topics with top data scientists on Facebook Live. In today's show, we talked about deep learning with Dr.