Neural Network in Azure ML

In this article written by Sumit Mund, author of the book Microsoft Azure Machine Learning, we will learn about neural network, which is a kind of machine learning algorithm inspired by the computational models of a human brain. It builds a network of computation units, neurons, or nodes. In a typical network, there are three layers of nodes. First, the input layer, followed by the middle layer or hidden layer, and in the end, the output layer. Neural network algorithms can be used for both classification and regression problems.

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The number of nodes in a layer depends on the problem and how you construct the network to get the best result. Usually, the number of nodes in an input layer is equal to the number of features in the dataset. For a regression problem, the number of nodes in the output layer is one while for a classification problem, it is equal to the number of class or label. Each node in a layer gets connected to all the nodes in the next layer. Each edge that connects between nodes is assigned a weight. So, a neural network can well be imagined as a weighted directed acyclic graph.

In a typical neural network, as shown in the preceding figure, the middle layer or hidden layer contains the number nodes, which are chosen to make the computation right. While there is no formula or agreed convention for this, it is often optimized after trying out different options.

Azure Machine Learning supports neural network for regression, two-class classification, and multiclass classification. It provides a separate module for each kind of problem and lets the users tune it with different parameters, such as the number of hidden nodes, number of iterations to train the model, and so on.

A special kind of neural network algorithms where there are more than one hidden layers is known as deep networks or deep learning algorithms. Azure Machine Learning allows us to choose the number of hidden nodes as a property value of the neural network module. These kind of neural networks are getting increasingly popular these days because of their remarkable results and because they allow us to model complex and nonlinear scenarios. There are many kinds of deep networks, but recently, a special kind of deep network known as the convolutional neural network got very popular because of its significant performance in image recognition or classification problems. Azure Machine Learning supports the convolutional neural network.

For simple networks with three layers, this can be done through a UI just by choosing parameters. However, to build a deep network like a convolutional deep network, it’s not easy to do so through a UI. So, Azure Machine Learning supports a new kind of language called Net#, which allows you to script different kinds of neural network inside ML Studio by defining different node, the connections (edges), and kind of connections. While deep networks are complex to build and train, Net# makes things relatively easy and simple.

Though complex, neural networks are very powerful and Azure Machine Learning makes it fun to work with these be it three-layered shallow networks or multilayer deep networks.

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Further resources on this subject:

• Security in Microsoft Azure [article]
• High Availability, Protection, and Recovery using Microsoft Azure [article]
• Managing Microsoft Cloud [article]