Summary

After covering most of the theoretical knowledge in the previous chapters, this chapter used a real-life case study to cement our knowledge. The idea is to encourage learning through practice with a hands-on approach.

The chapter started off by explaining the influence of deep learning on a wide range of industries where accuracy is required. One of the main industries driving deep learning's growth is banking and finance, where such algorithms are being used in domains such as the evaluation of loan applications, the detection of fraud, and the evaluation of past decision-making to predict future behavior, mainly due to the algorithm's ability to supersede human performance in these respects.

This chapter used a real-life dataset from a Taiwanese bank, with the objective of predicting whether a client would default on a payment. This chapter started developing a solution to this by explaining the importance of defining the what, why, and how of any data problem...