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machine learning - What is a fully convolution network? - Artificial . . . Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations Equivalently, an FCN is a CNN without fully connected layers Convolution neural networks The typical convolution neural network (CNN) is not fully convolutional because it often contains fully connected layers too (which do not perform the
What are the features get from a feature extraction using a CNN? So, the convolutional layers reduce the input to get only the more relevant features from the image, and then the fully connected layer classify the image using those features, isn't it? I think I've just understood how a CNN works
What is the fundamental difference between CNN and RNN? CNN vs RNN A CNN will learn to recognize patterns across space while RNN is useful for solving temporal data problems CNNs have become the go-to method for solving any image data challenge while RNN is used for ideal for text and speech analysis In a very general way, a CNN will learn to recognize components of an image (e g , lines, curves, etc ) and then learn to combine these components
deep learning - Artificial Intelligence Stack Exchange Why do we need convolutional neural networks instead of feed-forward neural networks? What is the significance of a CNN? Even a feed-forward neural network will able to solve the image classificat
Extract features with CNN and pass as sequence to RNN But if you have separate CNN to extract features, you can extract features for last 5 frames and then pass these features to RNN And then you do CNN part for 6th frame and you pass the features from 2,3,4,5,6 frames to RNN which is better The task I want to do is autonomous driving using sequences of images
When training a CNN, what are the hyperparameters to tune first? I am training a convolutional neural network for object detection Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I r
How to use CNN for making predictions on non-image data? You can use CNN on any data, but it's recommended to use CNN only on data that have spatial features (It might still work on data that doesn't have spatial features, see DuttaA's comment below) For example, in the image, the connection between pixels in some area gives you another feature (e g edge) instead of a feature from one pixel (e g color) So, as long as you can shaping your data
How to handle rectangular images in convolutional neural networks . . . I think the squared image is more a choice for simplicity There are two types of convolutional neural networks Traditional CNNs: CNNs that have fully connected layers at the end, and fully convolutional networks (FCNs): they are only made of convolutional layers (and subsampling and upsampling layers), so they do not contain fully connected layers With traditional CNNs, the inputs always need