CNN
[Capsule Network](https://texonom.com/capsule-network-a14fc7e569864154aae1ef44106e8991) [BCI](https://texonom.com/bci-e7b6d2a55d19435b9d3f8990441cab9e) 


## Convolutional Neural Network
Actually we use [Correlation](https://texonom.com/correlation-d75ea902ec294bf19e1180b6bfd22137) not the [Convolution](https://texonom.com/convolution-54def9d34de64e199ea46393675af2ce) 
Convolutional Layer와 Pooling Layer를 번갈아 가며 적용하여 입력 이미지의 특징을 추출하고  (Fully Connected) Layer를 통해 분류 작업을 수행
### Hyperparameters
- Convolution layer’s filter type, size, stride which result each activation map
- Placements and types of convolution layer, pooling layer and activation layer
So the typical architecture look like this
where N is usually up to ~5, M is large, 0 <= K <= 2. but recent advances challenge this paradigm
```type[(CONV-RELU)*N-POOL?]*M-(FC-RELU)*K,SOFTMAX```
### CNN Notion
|Title|
|:-:|
|[CNN History](https://texonom.com/cnn-history-76ffc514584846348136ea794f37ea9a)|
|[Pooling Layer](https://texonom.com/pooling-layer-c36550932e8c4436b4b6da0d4d2fc946)|
|[Convolutional Layer](https://texonom.com/convolutional-layer-0880b6c2089b4b20bb9a6232b6cb4b19)|
|[CNN bottleneck layer](https://texonom.com/cnn-bottleneck-layer-c67427171be5400db198dd6e7ef644ce)|
|[Training CNN](https://texonom.com/training-cnn-dc4f33cdf45544668d6f9cb6d059f431)|
|[Local Contrast Normalization](https://texonom.com/local-contrast-normalization-036406a996c94edab249e7ff8a32b2bc)|
|[ctivation map](https://texonom.com/ctivation-map-d0117ee2531b44dcbb0d19d33dff23ac)|



### CNN Models
|Title|
|:-:|
|[ResNet](https://texonom.com/resnet-d8c7707445684e848ebef939500672d4)|
|[ALexNet](https://texonom.com/alexnet-e33d746e06a540d4b8900be7ccede18c)|
|[ConvNets](https://texonom.com/convnets-1846cf4fac8b4feea6fa8cf4ac083df9)|
|[SuperVision](https://texonom.com/supervision-9d75ddb2bccd4dc9857f4332ae096298)|
|[GoogLeNet](https://texonom.com/googlenet-64b5ad51aafc48d492ba489f47ffa58a)|
|[VGG](https://texonom.com/vgg-c59b523e5e894bf2842474c9022dceec)|
|[MSRA](https://texonom.com/msra-9024cb9533e54bdc9ada6d30601f847b)|







