Now that we have solved the Titanic survival prediction problem with an acceptable level of accuracy, there are other practical aspects of this problem and of overall deep learning phenomena that need to be considered too. In this section, we will see some frequently asked questions that might be already in your mind. Answers to these questions can be found in Appendix A.
- Draw an ANN using the original artificial neurons that compute the XOR operation: A⊕ B. Describe this problem formally as a classification problem. Why can't simple neurons solve this problem? How does an MLP solve this problem by stacking multiple perceptrons?
- We have briefly seen the history of ANNs. What are the most significant milestones in the era of deep learning? Can we explain the timeline in a single figure?
- Can I use another deep learning framework for solving this Titanic survival prediction problem more flexibly?
- Can I use Name as a feature to be used in the MLP in the code?
- I understand the number of neurons in the input and output layers. But how many neurons should I set for the hidden layers?
- Can't we improve the predictive accuracy by the cross-validation and grid search technique?
