Como funciona o Deep Learning
It provides educational insights for researchers and practitioners in machine learning, but is incremental as it reviews existing knowledge.
This chapter addresses the lack of understanding of how deep learning methods work, why they are effective, and their limitations, by describing the transition from shallow to deep networks, providing implementation examples, and discussing theoretical background.
Deep Learning methods are currently the state-of-the-art in many problems which can be tackled via machine learning, in particular classification problems. However there is still lack of understanding on how those methods work, why they work and what are the limitations involved in using them. In this chapter we will describe in detail the transition from shallow to deep networks, include examples of code on how to implement them, as well as the main issues one faces when training a deep network. Afterwards, we introduce some theoretical background behind the use of deep models, and discuss their limitations.