Radial Basis Function Neural Network Scala Implementation

 

In this post, I am going to demonstrate a two-step Scala implementation of Radial Basis Function Neural Network (RBFNetwork): (unsupervised) k-means clustering first, and (supervised) gradient descent second. This two-step implementation is fast and efficient compared to Multilayer Perceptron while providing good predictive performance. This is because unsupervised learning at the first step provides information about data distribution so that the second step can have an intuition about the data and fine-tune the model.

For us, most of the Machine Learning models that we use today are just black box approaches that we take from some library. It is good to have this ability for faster development; however, it would be nicer to know internal dynamics of their implementations. Therefore, I am hoping that this post will be simple enough to provide you that information.

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Getting Started Deep Learning Revolution with Java

AI and deep learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. It is the technology behind self-driven cars, intelligent personal assistant computers, and decision support systems. Deep learning algorithms are being used across a broad range of industries. As the fundamental driver of AI, being able to tackle deep learning with Java is going to be a vital and valuable skill, not only within the tech world, but also for the wider global economy that depends upon knowledge and insight for growth and success.

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