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25 Feb 2024
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Convolutional Neural Networks – Different Kinds Of Networks

Convolutional Neural Networks – In daily life, we ​​carry out actions every day in a natural way. Behind them is artificial intelligence under machine learning models that are transparent to us. For example, the detection of a vehicle’s license plate when passing a parking lot or the facial detection of ourselves to be able to access a Smartphone are two clear cases of image identification through convolutional neural network algorithms. It is what we come to talk to you about in this post, don’t miss it!

How Convolutional Neural Networks (CNN) Began

The identification of objects in images is one of the classic problems of Artificial Intelligence. At the beginning of image identification technology, machine learning was based on neural networks and was more than enough to detect elements in small images. However, those same algorithms become inefficient when we increase the size of said images.

What Are Convolutional Neural Networks Or Convolutional Neural Networks?

The convolutional neural networks or convolutional neural networks solve this problem since they assume specific spatial characteristics of the inputs that simplify the network architectures, significantly reducing the number of input variables. Therefore, they are instrumental in computer vision problems, and in particular, in object recognition.

In 2012, a convolutional neural network won the ImageNet object recognition competition for the first time and by a large margin, consisting of classifying 1000 objects in a set of more than one million images. During the following years, new deep networks significantly increased the ability to organize pictures to the point that the problem is considered to be practically solved.

Layer Structure Of A Convolutional Neural Network And Its Operation

CNN’s use different types of layers or layers. The convolutional layer is the essential layer that gives the network its name.

This layer works from small three-dimensional filters, which move around the image, obtaining the outputs of the coating.

Let’s see an example for an RGB image of 32 pixels in width and height. The idea, in this case, has a size of 32x32x3, while the filter is more petite, 5x5x3. Filters always have the same depth as the image (in this case, 3) since they will travel through the first and second dimensions (step 1 of the image).

The filter goes through all possible positions in the image (image step 2), from left to right and from top to bottom, and for each class gets an activation or an output value.

The idea here is that the filter goes through the image and obtains relevant features while reducing the size of the resulting image.

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It is usual to apply several filters to obtain more features in each image position (step 3 of the image). The idea is that each of these filters receives specific image characteristics that will be important when obtaining a sufficiently expressive representation.

As can be seen, convolutional layers apply different filters on an input image and create new volumes, so the spatial properties of the image are maintained.

What are Convolutional Neural Networks?

The idea here is that, by configuring a network in this way, the different layers obtain a hierarchical representation of the features, with the first layers recognizing more straightforward elements in an image and the following ones receiving higher-level models from these. simple ingredients.

For example, the first few layers can detect edges so that successive layers can detect targets.

What are Convolutional Neural Networks?

The pooling layer (POOL) is a type of layer that is present in a large number of CNN architectures. Its utility reduces the representations obtained to become smaller and more manageable computationally, reducing the number of necessary parameters.

The last layer of a convolutional network for classification problems is fully connected since we need an output neuron for each class to tell us, for example, whether or not there is a car in any given image.

Conclusion

Artificial intelligence is mounting at a significantly faster rate. The digitization and the mobile networking systems overall work towards the process. The larger the image, the greater the number of pixels that produce an exponential increase in the input variables (features) that are impossible to handle by a neural network or convolutional neural networks of traditional architecture.

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