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 artificial neural networks
The stages of the training cycle (or epoch) for the backpropagation multilayer perceptron
How to implement an MLP from the ground up in Scala
The numerous configuration parameters and options available to create the MLP classification or regression model.
To evaluate the impact of the learning rate and the gradient descent momentum factor on the convergence of the training process.
How to apply a multilayer perceptron to the financial analysis of the fluctuation of currencies
An overview of the convolution neural network
The next chapter will introduce the concept of genetic algorithms with a complete 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...