Previously, in Chapter 6, Training a Single Neuron, we explored a model involving a single neuron and the concept of the perceptron. A limitation of the perceptron model is that, at best, it can only produce linear solutions on a multi-dimensional hyperplane. However, this limitation can be easily solved by using multiple neurons and multiple layers of neurons in order to produce highly complex non-linear solutions for separable and non-separable problems. This chapter introduces you to the first challenges of deep learning using the Multi-Layer Perceptron (MLP) algorithm, such as a gradient descent technique for error minimization, followed by hyperparameter optimization experiments to determine trustworthy accuracy.
The following topics will be covered in this chapter:
- The MLP model
- Minimizing the error
- Finding the best hyperparameters
