In recent years, deep neural networks have made substantial progress in object recognition. However, one issue with deep learning is that it is currently unclear which proposed framework is exaggerated for a specific hitch. As a result, distinct dispositions are attempt before one that produces satisfactory results is discovered. This paper described a distributed supervised learning method for finding the best network architecture by modifying specifications for a perceived task dynamically. In the case of the MNIST information gathering, it is shown that asynchronous supervised learning can agree on a solution space. Setting several hyperparameters can be time-consuming when constructing neural networks. In this post, we'll provide you with some tips and instructions for better organizing your hyperparameter tuning process, which should help you find a good setting for the hyperparameters much faster.
|Título de la publicación alojada||Proceedings - 2nd International Conference on Smart Electronics and Communication, ICOSEC 2021|
|Editorial||Institute of Electrical and Electronics Engineers Inc.|
|Número de páginas||7|
|ISBN (versión digital)||9781665433686|
|Estado||Publicada - 2021|
|Publicado de forma externa||Sí|
|Evento||2nd International Conference on Smart Electronics and Communication, ICOSEC 2021 - Trichy, India|
Duración: 7 set. 2021 → 9 set. 2021
Serie de la publicación
|Nombre||Proceedings - 2nd International Conference on Smart Electronics and Communication, ICOSEC 2021|
|Conferencia||2nd International Conference on Smart Electronics and Communication, ICOSEC 2021|
|Período||7/09/21 → 9/09/21|
Nota bibliográficaPublisher Copyright:
© 2021 IEEE.