Extracto
Content
1 Introduction
1.1 Motivation
2 The Architecture of ConvNets and Data Processing
2.1 The Convolutional Layer
2.1.1 Hyperparameters and filter weights
2.1.2 Activation functions und Biases
2.2 The Pooling Layer
2.3 The Fully-Connected Layer
2.4 Processing of colored images
3 Advantages of Convolutional Neural Networks
3.1 Parameter Reduction
3.1.1 Weight Sharing in Convolutional Layers
3.1.2 Dimensionality Reduction via Pooling
3.2 Object Detection
4 Application to the MNIST Dataset
5 Summary
6 Literature
7 Appendix
7.1 Python Code
Final del extracto de 26 páginas
- Citar trabajo
- Anónimo, 2020, The Architecture of Convnets and Data Processing. Advantages of Convolutional Neural Networks, Múnich, GRIN Verlag, https://www.grin.com/document/914160
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