Convolutional Neural Networks (CNN) is a deep learning model that is particularly suitable for image recognition, video analysis, natural language processing and other fields. The design of CNN is inspired by biological vision systems and uses a hierarchical structure to capture local features and global patterns in data.

1950s: Frank Rosenblatt proposed the Perceptron, one of the earliest neural network models.

1980s: Yann LeCun and others proposed LeNet-5, which was the first CNN successfully applied to handwritten digit recognition.

1998: Yann LeCun and others further developed LeNet-5 and proposed an improved version of LeNet-5 for handwritten postal code recognition.

2012: Alex Krizhevsky and others proposed AlexNet, the first CNN to achieve breakthrough results in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC).

2014: VGGNet achieved better results in ILSVRC, demonstrating the advantages of deeper network structures.

2014: Google proposed the Inception architecture (GoogLeNet), which improved the computing efficiency of the network by introducing the Inception module.

2015: Microsoft proposed ResNet (Residual Network), which solved the vanishing gradient problem in deep network training through residual connections.

So far: CNN continues to evolve, with the emergence of new network structures such as EfficientNet and Vision Transformer, as well as further optimization in various application fields.

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Input layer: receives raw data, such as the pixel values ​​of an image.

Convolution layer: Use convolution kernels to extract local features.

Activation layer: introduces nonlinearity, such as ReLU.

Pooling layer: Reduce the data dimension, reduce the amount of calculation, and prevent over-fitting.

Fully connected layer: maps features to final output, such as classification labels.

Output layer: outputs the final result of the network.

Convolution operation: slide the convolution kernel on the input data to extract local features.

Weight sharing: The same convolution kernel shares weights on the entire input data, reducing model parameters.

Pooling: Downsampling a local area, such as maximum pooling or average pooling.

Activation function: introduce nonlinearity, such as ReLU, Sigmoid, Tanh, etc.

Convolution kernel (Filter): The weight matrix used to extract features in the convolution layer.

Stride: The step size for the convolution kernel to move on the input data.

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LeNet-5: Early CNN model for handwritten digit recognition.

AlexNet: Introducing the ReLU activation function, reducing the number of parameters and improving training speed.

VGGNet: uses small convolution kernels and deeper network structure.

InceptionNet: Introducing the Inception module to improve the computing efficiency of the network.

ResNet: Solve the vanishing gradient problem in deep network training through residual connections.

SqueezeNet: Demonstrates that CNNs can maintain high performance even with a small number of parameters.

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CNN extracts local features of the image through multi-layer convolution and pooling operations, and performs classification through fully connected layers. Convolution operations can capture low-level features such as edges and textures in images, while deep networks can learn more complex patterns. Through weight sharing and pooling, CNN can effectively handle large data sets and reduce the risk of overfitting.

Image recognition: such as handwritten digit recognition, object recognition, etc.

Image segmentation: Segment the image into multiple regions for medical image analysis, etc.

Video analysis: used for behavior recognition, video surveillance, etc.

Speech recognition: Although CNN is mainly used for image processing, it can also be used for feature extraction of speech signals.

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Computing resource requirements: Deep networks require a large amount of computing resources and storage space.

Data volume requirements: In order to train a high-performance model, a large amount of annotated data is required.

Interpretability: The internal working mechanism of CNN is not as transparent as shallow models, making it difficult to explain its decision-making process.

Sensitive to input size: CNNs are somewhat sensitive to the size and scale of input data and may require preprocessing steps.

Local feature extraction: CNN is good at extracting local features, but may have difficulty capturing global context information.

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