11. Which of the following is a correct statement about a CNN (Convolution Neural Network) structure?以下哪個是關於卷積神經網路架構的正確說法？Number classification and identification is the main function of convolution layers and pooling layers, while fully connected layers are mainly used for image feature extraction. 卷積層與池化層的功能主要是數字分類辨識，而全連結層則主要用於擷取影像的特徵。There have to be more convolution layers than pooling layers. 卷積層的數量需要比池化層多。There have to be more fully connected layers than convolution and pooling layers. 全連接層的數量需要比卷積層與池化層多。Convolution layers are used to extract features from images, while fully connected layers are mainly used to make a classification decision. 卷積層主要是用來擷取影像的特徵，而全連結層則主要用於決定如何分類。

Which of the following Layers can be part of Convolution Neural Networks (CNNs)1 pointReluSoftmaxMaxpoolingDropoutAll of the above

Q.1 Which of the following are types of CNN architectures?1. RavNe2. AlexNet3. VGGNet4. JoNet

9. If you are now using a Convolution Neural Network (CNN) to distinguish Aaron Kwok and Andy Lau, which of the following is a typical data flow?你現在需要利用卷積神經網路判斷郭富城和劉德華，以下哪個是典型的數據流程呢？Image Input → Convolution Layer → Pooling Layer → Pooling Layer → Fully Connected Layer → Output 影像輸入 → 卷積層 → 池化層 → 池化層 → 全連接層 → 輸出Image Input → Pooling Layer → Convolution Layer → Fully Connected Layer → Output 影像輸入 → 池化層 → 卷積層 → 全連接層 → 輸出Image Input → Convolution Layer → Pooling Layer → Convolution Layer → Pooling Layer → Fully Connected Layer → Output 影像輸入 → 卷積層 → 池化層 → 卷積層 → 池化層 → 全連接層 → 輸出Image Input → Convolution Layer → Pooling Layer → Fully Connected Layer → Convolution Layer → Pooling Layer → Fully Connected Layer → Output 影像輸入 → 卷積層 → 池化層 → 全連接層 → 卷積層 → 池化層 → 全連接層 → 輸出

What statements are true?Group of answer choicesBy the end of our network, if we have multiple convolution and pooling layers, our final image is smaller than the original input image and we can classify the entire image, not just a region.Two common pooling methods are max pooling and mean pooling.If you use Photoshop to switch the location of the eyes and the mouth of a cat, a convolutional net with pooling layers would likely still "recognize" the image as a cat.Pooling introduces translation invariance by conveying what patterns are apparent in the image, without conveying where they are exactly.

Which of the following statements is TRUE about Convolution Neural Networks (CNNs)?1 pointCNN can be applied ONLY on Image dataCNN can be applied on ANY 2D and 3D array of data.CNN can be applied ONLY on Text and Speech data.CNN can be applied ONLY on Image and Text data.All of the above