Como funciona o Deep Learning

TL;DR

This paper explains how deep learning works, covering the transition from shallow to deep networks, implementation examples, training challenges, theoretical background, and limitations of deep models.

Contribution

It provides a comprehensive overview of deep learning fundamentals, implementation guidance, and discusses current limitations and theoretical aspects.

Findings

Deep learning has become the state-of-the-art in many classification tasks.

Training deep networks involves specific challenges and issues.

Theoretical understanding of deep models is still evolving.

Abstract

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.