Hands-on Image Recognition with CNN
碩士 === 元智大學 === 資訊管理學系 === 107 === Human beings are visualizers. The amount of information received from the visuals accounts for about 60% of all our senses. In the process of developing artificial intelligence, we train that machines what see the world, understand the world and use images recognit...
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| Format: | Others |
| Language: | zh-TW |
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2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/f7b527 |
| Summary: | 碩士 === 元智大學 === 資訊管理學系 === 107 === Human beings are visualizers. The amount of information received from the visuals accounts for about 60% of all our senses. In the process of developing artificial intelligence, we train that machines what see the world, understand the world and use images recognition as a source of data for making decision and judgment.
Deep learning is the mainstream of artificial intelligence, which a class of machine learning algorithms that use multiple layers to progressively extract higher level features from raw input. Artificial Neural Networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. Convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. The key to affecting the convolutional neural network is the architecture, the depth and the weight of the convolution kernel. The study compares these three factors and compares their impact differences.
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