Exploring Supervised Deep Learning
Chapters 2 to 6 explored the core workhorse behind deep learning (DL) technology and included some minimal technical implementations for easy digestion. It is important to understand the intricacies of how different neural networks (NNs) work. One reason is that when things go wrong with any NN model, you can identify what the root cause is and mitigate it. Those chapters are also important to showcase how flexible DL architectures are to solve different types of real-world problems. But what are the problems exactly? Also, how should we train a DL model effectively in varying situations?
In this chapter, we will attempt to answer the preceding two points specifically for supervised deep learning, but we will leave answering the same questions for unsupervised deep learning for the next chapter. This chapter will cover the following topics:
- Exploring supervised use cases and problem types
- Implementing neural network layers for foundational...
