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
Question
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
Solution
The true statement about Convolution Neural Networks (CNNs) is: "CNN can be applied on ANY 2D and 3D array of data."
CNNs are not limited to image data. They can be applied to any data that can be represented as a 2D or 3D array. This includes image data, but also other types of data like sound waves, time series data, etc. The key is that the data must have a grid-like topology, which is true for images (2D grid of pixels) and many other types of data.
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Which of the following are types of CNN architecture? Check all that apply:1 pointJoNetAlexNetVGGNetRavNe
Q.1 Which of the following are types of CNN architectures?1. RavNe2. AlexNet3. VGGNet4. JoNet
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
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