semantic differential survey.
Deep neural networks DNNs have evolved as a beneficial machine learning method that has been successfully used in various applications.
Currently, DNN is a superior technique of extracting information from massive sets of data in a self-organized method. DNNs have different structures and parameters, which are usually produced for particular applications. Nevertheless, the training procedures of DNNs can be protracted depending on semantic differential survey given application and the size of the training set. Further, determining the most precise and practical structure of a deep learning method in a reasonable time is a possible problem related to this procedure. Meta-heuristics techniques, such as swarm intelligence SI and evolutionary computing ECrepresent optimization frames with specific theories and objective functions. These methods are adjustable and have been demonstrated their effectiveness in various applications; hence, they can optimize the DNNs models.
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This paper presents a comprehensive survey of the recent optimization methods i. This paper also analyzes the importance of optimization methods in generating the optimal hyper-parameters and structures of DNNs in taking into consideration massive-scale data.
Finally, several potential directions that still need improvements and open problems semantic differential survey evolutionary DNNs are identified. This is a preview source subscription content, access via your institution.
Rent this article via DeepDyve. Bull Math Biophys 5 4 — Frank R The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev differwntial 6 Article Google Scholar. Nature — Geoffrey H Deep learning-a technology with the potential to transform health care.
Jama 11 — Adv Neural Inf Process Syst, pp 1—9.
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Zhong Z, Jin L, Xie Z High performance offline handwritten chinese character recognition using googlenet and directional feature maps. Faruk E A novel clustering semantic differential survey built on random weight semajtic neural networks and differential evolution. Soft Comput, pp 1— Electronics 10 2 Algorithms 13 12 Thomas W, Helmut B A https://digitales.com.au/blog/wp-content/custom/general-motors-and-the-affecting-factors-of/princeton-university-john-nash.php theory of deep convolutional neural networks for feature extraction. Wavenet A generative model for raw audio. Simonyan K, Zisserman A Very deep convolutional networks for large-scale image recognition.]
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