Challenges of Applying Deep Learning in Real-World Applications

Abstract

Due to development in technology, millions of devices (internet of things: IoTs) are generating a large amount of data (which is called as big data). This data is required for analysis processes or analytics tools or techniques. In the past several decades, a lot of research has been using data mining, machine learning, and deep learning techniques. Here, machine learning is a subset of artificial intelligence and deep learning is a subset of machine leaning. Deep learning is more efficient than machine learning technique (in terms of providing result accurate) because in this, it uses perceptron and neuron or back propagation method (i.e., in these techniques, solve a problem by learning by itself [with being programmed by a human being]). In several applications like healthcare, retails, etc. (or any real-world problems), deep learning is used. But, using deep learning techniques in such applications creates several problems and raises several critical issues and challenges, which are need to be overcome to determine accurate results.