Deep convolutional neural network

This is a mind map about deep convolutional neural networks. It is a feedforward neural network that contains convolutional calculations and has a deep structure. It is one of the important algorithms for deep learning.

Edited at 2023-12-18 19:26:04

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Deep convolutional neural network

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Deep convolutional neural network

definition

It is a deep learning model mainly used to process data with grid structure.

deep learning model

It is a type of machine learning model based on artificial neural networks (Artificial Neural Networks). They learn and extract the feature representation of input data through multi-level nonlinear transformation. These models usually contain multiple hidden layers (deep structures) and are therefore called deep learning models.

application

CNN has achieved great success in the field of computer vision and is widely used in tasks such as image recognition, object detection, and image generation.

Key concepts and components

Convolutional Layer

One of the most important layers in CNN

Convolutional layers extract local features of input data by using convolution operations. The convolution operation is implemented by sliding convolution kernels (filters) on the input data. Each convolution kernel is used to detect specific features in the input.

Pooling Layer

The pooling layer is used to reduce the spatial size of the convolutional layer output, reduce computational complexity, and extract important information of features.

Common pooling operations include max pooling (taking the maximum value in the neighborhood) and average pooling (taking the average value in the neighborhood)

Activation Function

After the convolutional layer, an activation function is usually applied to introduce nonlinear properties. Common activation functions include ReLU (Rectified Linear Unit), etc., which help the network learn non-linear relationships.

Fully Connected Layer

After the convolution and pooling layers, a fully connected layer is usually used to map the extracted features to the output categories. A fully connected layer connects all nodes in the previous layer to every node in the next layer.

Batch Normalization

Used to accelerate the training process of deep networks, by normalizing the data of each mini-batch, it helps to improve the stability and convergence speed of the network.

The structure of convolutional neural network

A typical CNN structure includes alternating convolutional and pooling layers, and finally a fully connected layer. The structure of the entire network usually consists of input layer, hidden layer and output layer.

weight sharing

The convolution kernels in the convolutional layer are shared, which means that they are used to extract features across the entire input space. This effectively reduces the number of parameters and improves the generalization ability of the network.