NEURAL NETWORKS TO CONVOLUTIONAL NEURAL NETWORKS: EXPANSION AND DETAILED EXPLANATION
Abstract
In the last century, scientists discovered several visual neurological features. The optic nerve has a local receptive field. The recognition of a whole picture is composed of multiple local recognition points. Different neurons have the ability to recognize different shapes, and the optic nerve has superposition ability. The pattern can be composed of low-level simple lines. Later, people found that after the operation of the concatenation, the process of optic nerve processing calculation can be well reflected. The LeNet-5, which was invented by LeCun in 1998 [1], can greatly enhance the recognition effect. This article mainly focuses on the neural network evaluation, from neural networks to convolutional neural networks, convolutional layer, the pooling layer, and the overall CNN structure.
About the Authors
A. Yakufujiang
Al-Farabi Kazakh National University
Kazakhstan
Kazakhstan
Azati Yakufujiang
F. Malikova
Al-Farabi Kazakh National University; Almaty University of Power Engineering and Telecommunications
Kazakhstan
Kazakhstan
A. Temirbekov
Al-Farabi Kazakh National University
Kazakhstan
Kazakhstan
S. Kenzhegulova
University of International Business
Kazakhstan
Kazakhstan
References
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Review
For citations:
Yakufujiang A., Malikova F., Temirbekov A., Kenzhegulova S. NEURAL NETWORKS TO CONVOLUTIONAL NEURAL NETWORKS: EXPANSION AND DETAILED EXPLANATION. Herald of the Kazakh-British Technical University. 2019;16(3):55-60.
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