Introduction to Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are a class of deep learning algorithms that have revolutionised the field of computer vision. They are designed to automatically and adaptively learn spatial hierarchies of features from input images, making them highly effective for tasks such as image classification, object detection, and segmentation.

The Architecture of CNNs

CNNs are composed of multiple layers, each serving a distinct purpose in the feature extraction process:

• Convolutional Layers: These layers apply convolution operations to the input image, using filters or kernels to detect local patterns such as edges, textures, and shapes.
• Pooling Layers: Pooling layers reduce the dimensionality of the feature maps while retaining important information. Common pooling techniques include max pooling and average pooling.
• Fully Connected Layers: After several convolutional and pooling layers, the high-level reasoning in the neural network is done via fully connected layers. These layers interpret the features extracted by previous layers to make final predictions.

The Power of Deep CNNs

The term “deep” in deep learning refers to the use of multiple layers in a neural network. Deep CNNs leverage this depth to learn increasingly abstract and complex representations of data. This capability has been pivotal in achieving state-of-the-art performance across various computer vision tasks.

For instance, deep CNNs have been instrumental in advancements such as:

• Image Classification: Recognising objects within images with high accuracy.
• Object Detection: >

• Image Segmentation: >

• Face Recognition: >

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