Summary

In this chapter, we introduced some fundamental themes of DL. We started our journey with a basic but comprehensive introduction to ML. Then we gradually moved on to DL and different neural architectures. Then we got a brief overview of the most important DL frameworks. Finally, we saw some frequently asked questions related to deep learning and the Titanic survival prediction problem.

In the next chapter, we'll begin our journey into DL by solving the Titanic survival prediction problem using MLP. Then'll we start developing an end-to-end project for cancer type classification using a recurrent LSTM network. A very-high-dimensional gene expression dataset will be used for training and evaluating the model.