neural networks
A convolutional neural network (CNN) that does not have fully connected layers is called a fully convolutional network (FCN). See this answer for more info. An example of an FCN is the u-net,
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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
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convolutional neural networks
0 I''m building an object detection model with convolutional neural networks (CNN) and I started to wonder when should one use either multi-class CNN or a single-class CNN.
machine learning
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
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machine learning
The concept of CNN itself is that you want to learn features from the spatial domain of the image which is XY dimension. So, you cannot change dimensions like you mentioned.
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
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Why would "CNN-LSTM" be another name for RNN, when it doesn''t even have RNN in it? Can you clarify this? What is your knowledge of RNNs and CNNs? Do you know what an LSTM is?
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How do I handle large images when training a CNN?
Suppose that I have 10K images of sizes $2400 times 2400$ to train a CNN. How do I handle such large image sizes without downsampling? Here are a few more specific questions. Are
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What is the fundamental difference between CNN and 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
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What is the difference between a convolutional neural network and a
A convolutional neural network (CNN) is a neural network where one or more of the layers employs a convolution as the function applied to the output of the previous layer.
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