improve prompt
This commit is contained in:
@@ -20,7 +20,7 @@ Please perform the following operations based on the provided Markdown and PDF:
|
|||||||
5. **Adjust headings**: Based on the different content within sub-chapters, rename headings with the same name to avoid duplicate headings and ensure a clear outline.
|
5. **Adjust headings**: Based on the different content within sub-chapters, rename headings with the same name to avoid duplicate headings and ensure a clear outline.
|
||||||
6. **Clean up redundant headings**: If there is no content between adjacent headings of the same level, the headings should be adjusted to conform to standards.
|
6. **Clean up redundant headings**: If there is no content between adjacent headings of the same level, the headings should be adjusted to conform to standards.
|
||||||
|
|
||||||
- For example, the following is improper formatting, where there is no content between multiple headings of the same level
|
- For example, the following format is incorrect, there is no content between multiple peer headings, and there are duplicate heading names.
|
||||||
|
|
||||||
```
|
```
|
||||||
## Convolutional Neural Networks: Weight Sharing with Multiple Filters
|
## Convolutional Neural Networks: Weight Sharing with Multiple Filters
|
||||||
@@ -55,8 +55,6 @@ Please perform the following operations based on the provided Markdown and PDF:
|
|||||||
|
|
||||||
This diagram consists of two parts. The left part illustrates how multiple filters (represented by connections of different colors) are applied to an input image, with each filter detecting a different pattern. The right part shows how a single filter (hidden unit / filter response) is convolved over the input to produce a feature map.
|
This diagram consists of two parts. The left part illustrates how multiple filters (represented by connections of different colors) are applied to an input image, with each filter detecting a different pattern. The right part shows how a single filter (hidden unit / filter response) is convolved over the input to produce a feature map.
|
||||||
|
|
||||||
## Convolutional Neural Networks
|
|
||||||
|
|
||||||
### Weight Sharing and Translation Invariance
|
### Weight Sharing and Translation Invariance
|
||||||
|
|
||||||
#### Translation invariance:
|
#### Translation invariance:
|
||||||
|
|||||||
Reference in New Issue
Block a user