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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.

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?

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.

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, which does not use any fully connected layers, but only convolution, downsampling (i.e. pooling), upsampling (deconvolution), and copy and crop operations.

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.

21 I was surveying some literature related to Fully Convolutional Networks and came across the following phrase, A fully convolutional network is achieved by replacing the parameter rich fully connected layers in standard CNN architectures by convolutional layers with 1 × 1 1 × 1 kernels. I have two questions. What is meant by parameter rich?

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.

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 ...

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.

Typically for a CNN architecture, in a single filter as described by your number_of_filters parameter, there is one 2D kernel per input channel. There are input_channels * number_of_filters sets of weights, each of which describe a convolution kernel. So the diagrams showing one set of weights per input channel for each filter are correct.

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