Summary

In this chapter, you were introduced to the basic concepts of ANNs and DL. After getting acquainted with the Iris dataset and the MLP classifier, you were presented with the notion of network architecture optimization. Next, we demonstrated a genetic algorithm-based optimization of network architecture for the MLP classifier. Finally, we were able to combine network architecture optimization with model hyperparameter tuning using the same genetic algorithms approach, thereby enhancing the performance of the classifier even further.

So far, we have concentrated on supervised learning (SL). In the next chapter, we will look into applying genetic algorithms to reinforcement learning (RL), an exciting and fast-developing branch of ML.