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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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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.

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.

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. ‍

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The most common use for CNNs is image classification, for example identifying satellite images that contain roads or classifying hand written letters and digits. There are other quite mainstream tasks such as image segmentation and signal processing, for which CNNs perform well at.

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.