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The Convolutional Neural Network (CNN or ConvNet) is a subtype of Neural Networks that is mainly used for applications in image and speech recognition. Its built-in convolutional layer reduces the high dimensionality of images without losing its information. That is why CNNs are especially suited for this use case.

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As opposed to MLPs, CNNs have the following distinguishing features: 3D volumes of neurons. The layers of a CNN have neurons arranged in 3 dimensions: width, height and depth. Where each neuron inside a convolutional layer is connected to only a small region of the layer before it, called a receptive field.

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Convolutional Neural Networks are deep learning models designed specifically for processing & analyzing visual data such as images & videos. It is inspired by the structure of the visual cortex in the brain - that consists the cells sensitive to small regions of the visual field. ‍

Why do we use CNN? A. We use CNNs (Convolutional Neural Networks) in image processing because they can effectively extract features from images and learn to recognize patterns, making them well-suited for tasks such as object detection, image segmentation, and classification.

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Q2. What is the main advantage of CNN? A. The main advantage of using CNNs is that they do not require human supervision for image classification and identifying important features in images.

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What are the advantages of convolutional neural networks?

• No require human supervision required.
• Automatic feature extraction.
• Highly accurate at image recognition & classification.
• Weight sharing.
• Minimizes computation.
• Uses same knowledge across all image locations.
• Ability to handle large datasets.
• Hierarchical learning.

Benefits of Using CNNs for Machine and Deep Learning The CNN architecture is especially useful for image recognition and image classification, as well as other computer vision tasks because they can process large amounts of data and produce highly accurate predictions.