| ![docs/examples/multi-line_en_white.png](./docs/examples/multi-line_en_white.png) | This chapter is currently only available in this web version. ebook and print will follow.<br/>Convolutional neural networks learn abstract features and concepts from raw image pixels. Feature<br/>Visualization visualizes the learned features by activation maximization. Network Dissection labels<br/>neural network units (e.g. channels) with human concepts. |
| ![docs/examples/multi-line_en_black.png](./docs/examples/multi-line_en_black.png) | transforms the image many times. First, the image goes through many convolutional layers. In those<br/>convolutional layers, the network learns new and increasingly complex features in its layers. Then the <br/>transformed image information goes through the fully connected layers and turns into a classification<br/>or prediction. |
| ![docs/examples/multi-line_en_black.png](./docs/examples/multi-line_en_black.png) | transforms the image many times. First, the image goes through many convolutional layers. In those<br/>convolutional layers, the network learns new and increasingly complex features in its layers. Then the <br/>transformed image information goes through the fully connected layers and turns into a classification<br/>or prediction. |
## 安装
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@@ -47,19 +47,14 @@ English [README](./README_en.md) (`out-dated`).