Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

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.

Automated summary

Deep Learning (DL) is a subfield of machine learning that has a significant impact on extracting new knowledge. DL enables the extraction of advanced data representations and knowledge, leading to the discovery of hidden knowledge. Due to its excellent performance and accuracy, DL has a promising future. Understanding the fundamentals and state-of-the-art of DL is essential for effective utilization. This paper provides a survey on DL techniques, advantages, drawbacks, architectures, and methods, offering a straightforward and clear understanding from different perspectives. Additionally, it compares existing methods and explores DL applications in fields such as medical image analysis and handwriting recognition.