Most of the companies developed the application that is capable of using the deep neural networks, which requires a lot of calculation. For that reason, they regularly show up in programming as administration applications on GPU controlled servers. The application that should keep running without any internet access, utilizing systems on servers is impractical.
Several Multinational Corporation showed how they work process is done by hitting neural networks on mobile devices. An American Multinational Technology Company, Apple declared their CoreML platform at WWDC 2017, using such platform you can train machine learning model into your app.
An American Multinational Company specialized in internet related service and product, Google is operating their mobile device named “Tensorflow Lite”. The Tensorflow Lite is the version of the Tensorflow toolkit for mobile devices. The company also exposed multiple pre-trained image recognition models.
Google developers are yet constrained in the alternatives for building quicker applications with neural systems. One approach is reducing the accuracy as well as the size of their network and another one is reducing the floating point precision after training.
One of the researchers of the company has come up with the unique as well as a novel idea called “Co-train two neural networks”, where One network is a full neural network known the trainer network and another network is known as projection network. Here, projection network is a system that tries to express the information as well as transitional portrayals of the mentor organize in a low-memory portrayal.
The name of the researcher is Sujith Ravi. The network systems together are trained at the same time, share the same loss function. This technique helps the projection network to learn from the trainer network. The huge network can remain on the server when both neural networks are ready to be used and the user can download the well-organized network on the smartphone.