MXNet Tutorial: Complete Guide with Hands-On Implementation of Deep Learning Framework

MXNet Tutorial: Complete Guide with Hands-On Implementation of Deep Learning Framework

The existing frameworks are programming language-specific. This problem is overcome by MXNet framework and it provides one system for different programming flavor. As the popularity and need for deep learning networks increase, there has been a lot of effort to build tools that ease the development of deep learning models. One such tool that we will discuss today is MXNet. You might be wondering what makes MXNet better than the already existing deep learning frameworks like Theano or Caffe.

As the popularity and need for deep learning networks increase, there has been a lot of effort to build tools that ease the development of deep learning models. One such tool that we will discuss today is MXNet. You might be wondering what makes MXNet better than the already existing deep learning frameworks like Theano or Caffe. The existing frameworks are programming language-specific. This problem is overcome by MXNet and it provides one system for different programming flavours.  

In this article, we will look into 

  • Why MXNet?
  • A complete overview of MXNet
  • Implementation of MXNet on random data

Why MXNet?

MXNet is an open-source deep learning framework that is used to define, train and deploy neural networks. MXNet is short for mix-net because this framework was developed by combining various programming approaches into one. This framework supports Python, R, C++, Julia, Perl and many other languages which eliminates the need to learn new languages in order to use different frameworks. 

Another advantage is that the models built using MXNet are portable such that they can fit in small amounts of memory. So, once your model is trained and tested, it can be easily deployed to mobile devices or connected systems. MXNets are scalable to be used on multiple machines and GPU simultaneously. This is why Amazon has chosen this framework for its deep learning web services. 


developers corner deep learning frameworks deep learning mxnet

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

Looking to attend an AI event or two this year? Below ... Here are the top 22 machine learning conferences in 2020: ... Start Date: June 10th, 2020 ... Join more than 400 other data-heads in 2020 and propel your career forward. ... They feature 30+ data science sessions crafted to bring specialists in different ...

Hire Machine Learning Developers in India

We supply you with world class machine learning experts / ML Developers with years of domain experience who can add more value to your business.

Hire Machine Learning Developer | Hire ML Experts in India

We supply you with world class machine learning experts / ML Developers with years of domain experience who can add more value to your business.

LinkedIn’s Open Source Deep Learning Framework

U.S. social networking company LinkedIn has released DeText, an open source natural language processing framework that uses deep neural networks to facilitate tasks such as search.

Deep Learning Frameworks: MxNet vs TensorFlow vs DL4j vs PyTorch

We will go through some of the popular deep learning frameworks like Tensorflow, MxNet, DL4j, PyTorch and CNTK so you can choose which one is best for your project. Deep Learning is a branch of Machine Learning. Though machine learning has various algorithms, the most powerful are neural networks. To build these neural networks, we use different frameworks like Tensorflow, CNTK, and MxNet.