Exercises

This chapter was filled with various theoretical concepts to understand so, just like the previous chapter, don't skip the exercises:

1. What are the similarities between artificial and biological neurons?
2. Does the neuron's topology change the neural network's behavior?
3. Why do neurons require a non-linear activation function?
4. If the activation function is linear, a multi-layer neural network is the same as a single layer neural network. Why?
5. How is an error in input data treated by a neural network?
6. Write the mathematical formulation of a generic neuron.
7. Write the mathematical formulation of a fully connected layer.
8. Why can a multi-layer configuration solve problems with non-linearly separable solutions?
9. Draw the graph of the sigmoid, tanh, and ReLu activation functions.
10. Is it always required to format training set labels into a one-hot encoded representation...