This paper examines the effectiveness of applying deep learning through a variety of process (eg, medical and manufacturing steel mills, semiconductor processes, general chatbots, and financial industries) data.
The most important thing in deep learning is the legacy architecture of the system to be applied, and in the manufacturing industry, understanding the process is the most important.
This is because deep learning models applied to processes that improve efficiency and that can be directly linked to productivity gains can generate enormous cost savings. I don't know much about the steel industry I used to, but I compare the processes of the semiconductor industry and consider where deep learning algorithms might be applied.
In addition, in the medical industry, security is important above all, and Federated Learning should be used, and in the case of chatbots, an overall overview of the importance of architectural design according to the Intent Scope and the transfer learning will come out.