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.
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