A Convolutional Neural Network (CNN) is a specialized type of neural network primarily used for processing structured grid data, such as images. CNNs are designed to automatically and adaptively learn spatial hierarchies of features through backpropagation.
The core components of a CNN include:
Convolutional Layers: These layers apply convolutional operations to input data, utilizing filters (kernels) that slide over the input to detect patterns like edges or textures. Each filter learns to identify specific features.
Activation Function: After convolution, an activation function (often ReLU) is applied to introduce non-linearity, allowing the network to learn complex patterns.
Pooling Layers: These layers reduce the dimensionality of the feature maps, preserving the most important information while reducing computational load. Max pooling is a common technique used here.
Fully Connected Layers: After several convolutional and pooling layers, the high-level reasoning in the network is performed by fully connected layers, which connect every neuron in one layer to every neuron in the next.
CNNs excel in tasks such as image classification, object detection, and facial recognition, thanks to their ability to learn hierarchical feature representations. They have revolutionized computer vision and are widely used in various applications. #ConvolutionalNeuralNetwork #CNN #AI #Finance #AlgoTrading
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