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Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible. Highly effective DL techniques help to find more hidden knowledge. Deep learning has a promising future due to its great performance and accuracy. We need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively. A survey on DL ways, advantages, drawbacks, architectures, and methods to have a straightforward and clear understanding of it from different views is explained in the paper. Moreover, the existing related methods are compared with each other, and the application of DL is described in some applications, such as medical image analysis, handwriting recognition, and so on.

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Due to apparent flexibility of Intuitionistic Fuzzy Set (IFS) concepts in dealing with the imprecision or uncertainty, these are proving to be quite useful in many application areas for a more human consistent reasoning under imperfectly defined facts and imprecise knowledge. In this paper, we apply notions of entropy and intuitionistic fuzzy sets to present a new fuzzy decision-making approach called intuitionistic fuzzy entropy measure for selection and ranking the suppliers with respect to the attributes. An entropy-based model is formulated and applied to a real case study aiming to examine the rankings of suppliers. Furthermore, the weights for each alternative, with respect to the criteria, are calculated using intuitionistic fuzzy entropy measure. The supplier with the highest weight is selected as the best alternative. This proposed model helps the decision-makers in better understanding of the weight of each criterion without relying on the mere expertise.

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