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Convolutional Neural Network (CNN)
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.
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One Hundred Years of Solitude is the masterpiece of Gabriel Garcia Marquez. Reading this book begins with making sense of the characters' relationships, which are centered on the Buendía family and tells the story of the family's prosperity and decline, internal relationships and political struggles, self-mixing and rebirth over the course of a hundred years.
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Convolutional Neural Network (CNN)
- One Hundred Years of Solitude Character Relationship Chart
One Hundred Years of Solitude is the masterpiece of Gabriel Garcia Marquez. Reading this book begins with making sense of the characters' relationships, which are centered on the Buendía family and tells the story of the family's prosperity and decline, internal relationships and political struggles, self-mixing and rebirth over the course of a hundred years.
- One Hundred Years of Solitude Character Relationship Chart
One Hundred Years of Solitude is the masterpiece of Gabriel Garcia Marquez. Reading this book begins with making sense of the characters' relationships, which are centered on the Buendía family and tells the story of the family's prosperity and decline, internal relationships and political struggles, self-mixing and rebirth over the course of a hundred years.
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Project management is the process of applying specialized knowledge, skills, tools, and methods to project activities so that the project can achieve or exceed the set needs and expectations within the constraints of limited resources. This diagram provides a comprehensive overview of the 8 components of the project management process and can be used as a generic template for direct application.
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- Outline
Convolutional Neural Network (CNN)
Introduction
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.
development path
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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Hierarchy
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.
Detailed explanation of core concepts
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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Typical CNN model
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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principle
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.
application
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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technical limitations
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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