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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 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 can neural networks deal with varying input sizes? Whereas the original question is fairly open-ended, the answers focus primarily on NLP However, I stumbled on this question while looking how to do variable size image inputs for a CNN Variable size inputs are indeed possible for a convolutional approach - albeit with some caveats, and the stats stackexchange link above explores that related, alternate line of inquiry
16. 2. 5 Check Your Understanding – Network Attacks Answers 1 Angela, an IT staff member at ACME Inc , notices that communication with the company’s web server is very slow After investigating, she determines that the cause of the slow response is a computer on the internet sending a very large number of malformed web requests to ACME’S web server What type of attack is described in this scenario? access attack denial of service (DoS) attack
machine learning - How do neural networks learn specific features . . . That convolution responds to certain arrangements of these 1st-level features, e g two adjacent edges with different orientations are a corner You can think of the CNN-layers as a hierarchy where initial layers provide basic features the next layer detects compositions of these, the next layer detects compositions of the compositions and so on
What is the difference between CNN-LSTM and RNN? 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?
In a CNN, does each new filter have different weights for each input . . . 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