What is a Convolutional Neural Network?

What is a Convolutional Neural Network from Coding compiler. The Convolutional Neural Network is a special form of the artificial neural network. It has multiple convolutional layers and is well suited for machine learning and Artificial Intelligence (AI) applications in the field of image and speech recognition.

What is a Convolutional Neural Network?

Convolutional Neural Network in German means “folded neural network”. The abbreviation is CNN. It is a special form of artificial neural network specifically designed for machine learning and the processing of image or audio data.

The mode of operation is to some extent based on biological processes. The structure is similar to the visual cortex of the brain. The Convolutional Neural Network consists of several layers.

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These layers are convolutional layers, subsampling layers and fully meshed layers. Training a Convolutional Neural Network is usually supervised. One of the founders of the Convolutional Neural Network is Yann LeCun.

Building a Convolutional Neural Network

Conventional neural networks consist of fully or partially meshed neurons in multiple levels. These structures reach their limits when processing images because there should be a number of inputs corresponding to the number of pixels.

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The number of layers and the connections between them would be huge and could only be handled by computers with very high performance. The Convolutional Neural Network is composed of different layers and is the basic principle of a locally locally meshed neural feedforward network. The individual layers of the CNN are:

  • The convolutional layer,
  • The pooling layer and
  • The completely meshed layer.

The pooling layer follows the convolutional layer and can be present in this combination several times in succession. Since the pooling layer and the convolutional layer are locally meshed subnets, the number of links in these layers remains limited and manageable, even with large input quantities. The conclusion is a completely meshed layer.

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The tasks of the individual layers

The convolutional layer is the actual folding plane. It is able to recognize and extract individual characteristics in the input data. In image processing, these may be features such as lines, edges, or certain shapes. The processing of the input data takes place in the form of a matrix. Matrices of defined size (width x height x channels) are used.

The pooling layer, also known as subsampling layer, compresses and reduces the resolution of the detected features. For this purpose, the layer uses methods such as maximum pooling or mean pooling. Pooling discards unnecessary information and reduces the amount of data. It does not reduce the performance of machine learning. Due to the reduced data volume, the calculation speed increases.

The conclusion of the Convolutional Neural Network is the fully linked layer. It follows the repetitive sequences of convolutional and pooling layers. All features and elements of the upstream layers are linked to each output feature. The fully connected neurons may be arranged in multiple planes. The number of neurons depends on the classes or objects that should differentiate the neural network.

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Benefits of a Convolutional Neural Network in the area of ​​image recognition

Compared to traditional unfolded neural networks, the Convolutional Neural Network offers many advantages. It is suitable for machine learning and artificial intelligence applications with large amounts of input data such as image recognition.

The network is robust and insensitive to distortion or other optical changes. The CNN can process images taken in different lighting conditions and in different perspectives. It still recognizes the typical features of an image.

Since the CNN is divided into several local, partially meshed layers, it has a much smaller storage space requirement than fully meshed neural networks. The convolution layers dramatically reduce storage requirements. Also greatly reduced is the training time of the Convolutional Neural Networks.

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With the use of modern graphics processors, CNNs can be trained very efficiently. CNNs are the state-of-the-art machine learning and classification method in image recognition.

The method of operation using the example of image recognition

The CNN recognizes and extracts features of the input images using filters. The recognition of the structures is location-independent within the image. First, the CNN recognizes simple structures such as lines, splashes of color or edges in the first levels.

In the other levels, the Convolutional Neural Network learns combinations of these structures, such as simple shapes or curves. With each level, more complex structures can be identified. The data is scanned and filtered again and again in the layers. In the final step, the results are assigned to the classes or objects to be recognized.

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Areas of application of the Convolutional Neural Network

The most important application for Convolutional Neural Networks is image recognition. The artificial neural networks are used, for example, in the area of ​​facial recognition and object recognition. Another important application is speech recognition. The CNN’s achieve very good results in semantic parsing, in the classification and modeling of sentences or in machine translation.

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