Summary
This concludes not only the journey inside the multilayer perceptron, but also the introduction of the supervised learning algorithms. In this chapter, you learned:
- The components and architecture of a neural networks
- The stages of the training cycle of a backpropagation multilayer perceptron
- How to implement an MLP from the ground up in Scala
- The numerous configuration parameters and options to use MLP as a classifier and regression
- To evaluate the impact of the learning rate and the gradient descent momentum factor on the convergence of the sum of squared errors during training
- How to apply a multilayer perceptron to the financial analysis of the fluctuation of currencies
The next chapter will introduce the concept of genetic algorithms with a full implementation in Scala. Although, strictly speaking, genetic algorithms do not belong to the family of machine learning algorithms, they play a crucial role in the optimization of nonlinear, nondifferentiable problems and the selection of...
