Optimization for Deep Learning Algorithms: A Review2018 Feb 19
Optimization for Deep Learning Algorithms: A Review
ABSTRACT: In past few years, deep learning has received attention in the field of artificial intelligence. This paper reviews three focus areas of learning methods in deep learning namely supervised, unsupervised and reinforcement learning. These learning methods are used in implementing deep and convolutional neural networks. They offered unified computational approach, flexibility and scalability capabilities. The computational model implemented by deep learning is used in understanding data representation with multiple levels of abstractions. Furthermore, deep learning enhanced the state-of-the-art methods in terms of domains like genomics. This can be applied in pathway analysis for modelling biological network. Thus, the extraction of biochemical production can be improved by using deep learning. On the other hand, this review covers the implementation of optimization in terms of meta-heuristics methods. This optimization is used in machine learning as a part of modelling methods.
In this review, discussed about deep learning techniques which implementing multiple level of abstraction in feature representation. Deep learning can be characterized as rebranding of artificial neural network. This learning methods gains a large interest among the researchers because of better representation and easier to learn tasks. Even though deep learning is implemented, however there are some issues has been arise_. There are easily_ getting stuck at local optima and computationally expensive. DeepBind algorithm shows that deep learning can cooperate in genomics study. It is to ensure on achieving high level of prediction protein binding affinity. On the other hand, the optimization method which has been discusses consists of several meta-heuristics
methods which can be categorized under evolutionary algorithms. The application of the techniques involvedCRO shows the diversity of optimization algorithm to improve the analysis of modelling techniques. Furthermore, these methods are able to solve the problems arise in conventional neural network as it provides high quality in finding solution in a given search space. The application of optimization methods enable the
extraction of biochemical production of metabolic pathway. Deep learning will gives a good advantage in the biochemical production as it allows high level abstraction in cellular biological network. Thus, the use of CRO will improve the problems arise in deep learning which are getting stuck at local optima and it is computationally expensive. As CRO use global search in the search space to identify global minimum point. Thus, it will improve the training process in the network on refining the weight in order to have minimum error.