Abstract
Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. Besides, this study also discusses the challenges of designing and training deep neural networks.
Highlights
Deep learning methods are a group of machine learning methods that can learn features hierarchically from lower level to higher level by building a deep architecture
There are a large number of related approaches. These algorithms can be grouped into two categories based on their architectures: restricted Boltzmann machines (RBMs) and convolutional neural networks (CNNs)
Where θ = {W, b, c} are the parameters of RBM and they need to be learned during the training procedure; W denotes the weights between the visible layer and hidden layer; b and c h0
Summary
Deep learning methods are a group of machine learning methods that can learn features hierarchically from lower level to higher level by building a deep architecture. The deep learning methods have the ability to automatically learn features at multiple levels, which makes the system be able to learn complex mapping function f : X → Y directly from data, without help of the human-crafted features This ability is crucial for high-level feature abstraction since highlevel features are difficult to be described directly from raw training data. There are mainly three crucial reasons for the rapid development of deep learning applications nowadays: the big leap of deep learning algorithms, the significantly increased computational abilities, and the sharp drop of price in hardware This survey provides an overview of several deep learning algorithms and their emerging applications in several specific areas, featuring face recognition, road crack detection, fault diagnosis, and falls detection.
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