Como funciona o Deep Learning
arXiv:1806.07908
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.
arXiv commentBook chapter, in Portuguese, 31 pages
References in corpus (8)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Improving neural networks by preventing co-adaptation of feature detectors
- ADADELTA: An Adaptive Learning Rate Method
- On the difficulty of training Recurrent Neural Networks
- Conditional Random Fields as Recurrent Neural Networks
- Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
- Understanding Deep Convolutional Networks
- Are Accuracy and Robustness Correlated?