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Azienda News:
- What is the difference between a convolutional neural network and a . . .
A CNN, in specific, has one or more layers of convolution units A convolution unit receives its input from multiple units from the previous layer which together create a proximity Therefore, the input units (that form a small neighborhood) share their weights The convolution units (as well as pooling units) are especially beneficial as:
- machine learning - What is a fully convolution network? - Artificial . . .
A fully convolutional network is achieved by replacing the parameter-rich fully connected layers in standard CNN architectures by convolutional layers with $1 \times 1$ kernels I have two questions What is meant by parameter-rich? Is it called parameter rich because the fully connected layers pass on parameters without any kind of "spatial
- 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 while RNN is used for ideal for text and speech analysis
- What are the features get from a feature extraction using a CNN?
By accessing these high-level features, you essentially have a more compact and meaningful representation of what the image represents (based always on the classes that the CNN has been trained on) By visualizing the activations of these layers we can take a look on what these high-level features look like
- 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
- convolutional neural networks - keras model accuracy not improving . . .
Based on having 16 classes, the naïve accuracy of a dummy classifier would be around 1 16 (6 25%) assuming class balance and the dummy classifier predicts each class according to uniform probabilities (see scikit-learn article on dummy classifiers)
- When training a CNN, what are the hyperparameters to tune first?
Firstly when you say an object detection CNN, there are a huge number of model architectures available Considering that you have narrowed down on your model architecture a CNN will have a few common layers like the ones below with hyperparameters you can tweak: Convolution Layer:- number of kernels, kernel size, stride length, padding
- convolutional neural networks - When to use Multi-class CNN vs. one . . .
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 That is, if I'm making e g a
- How is the depth of a convolutional layer determined?
If you have a CNN, one single convolution operation would be pointless: since it is used for the whole image information, it can generalize, but only to specific (meaning: a finite amount of) features Easy example: if a 7x7 filter in the first layer concentrates on round shapes, it can not generalize on let's say red cubes at the same time
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