This time, I was finally able to figure out the CNN convolutional neural network!

Convolutional neural networks (CNNs) are a powerful deep learning architecture that is well suited for image classification and object recognition tasks. With the ability to automatically extract relevant features, process noisy images, and leverage pre-trained models, CNNs demonstrate state-of-the-art performance in a range of computer vision tasks, and in this article we will break them down in simple terms.

What is a Convolutional Neural Network (CNN)?

A convolutional neural network (CNN) is an artificial neural network for image recognition and processing, inspired by the biological processes of the visual cortex of animals. They are made up of neurons with learnable weights and biases.

CNNs use a technique called convolution, which is a special kind of linear operation, in at least one layer, rather than matrix multiplication in general.

Figure 1: A neural network with multiple convolutional layers

How convolutional neural networks work:

CNNs apply filters (small rectangles) to the input image to detect features such as edges or shapes. The filter slides across the width and height of the input image and calculates the dot product between the filter and the input to generate an activation map.

The activation plot is fed into the pooling layer, and the graph is downsampled to reduce the number of dimensions. This makes the model more efficient and robust. The last layer is the fully connected layer, which classifies the input images into categories such as "dogs" or "cats".

Some popular CNN architectures include AlexNet, VGGNet, ResNet, and Inception. These have been used to solve complex problems, such as identifying thousands of objects or detecting diseases through medical scans.

To build a CNN, you can define the schema by selecting hyperparameters such as the number of filters, filter size, stride length, and pooling size.

You then train the network on a large dataset and use backpropagation to update weights and biases.

With sufficient data and computing power, CNNs can achieve superhuman performance on many vision tasks.

CNNs have revolutionized computer vision and are being used by companies like Google, Facebook, and others to enhance image recognition capabilities in applications and services.

They have become an indispensable tool for any machine learning practitioner.

Figure 2: Convolutional neural network-CNN architecture

Number of layers of a convolutional neural network:

1. Convolutional layer

The convolutional layer applies convolution operations to the input, passing filters across the entire image. This filter detects features such as edges or curves in the image. Multiple filters can detect different features.

Convolution operations combine inputs and filters to create feature maps. This shows the location and intensity of the detected features. By stacking multiple convolutional layers, the network can detect higher-level, more complex features.

Convolution operations combine inputs and filters to create a feature map. This shows the location and intensity of the detected features. By stacking multiple convolutional layers, the network can detect higher-level and more complex features.

Figure 3: Convolutional layer

2. Pooling layer

The pooled layer is inserted between the convolutional layers. They downsampled the feature map to reduce the number of parameters, control overfitting, and keep the network constant for small translations.

The most common types are max pooling and average pooling. The pooling layer subsamples the feature map, retaining only the most important information.

By cleverly stacking multiple convolutional and pooling layers, CNNs can learn to detect complex features in images, such as faces, objects, scenes, and more. The output of the final convolutional layer is then flattened into a single vector and passed to the fully connected layer classification.

Spatial pooling, also known as subsampling or downsampling, reduces the dimensionality of each map but preserves important information. Space pools can be of different types:

• Maximum pondification Maximum pondification
• Average Pond Pondification Average Pondification
• Sum Pool: Sum pooling

Figure 4: MaxPooling Layer Figure 4: MaxPooling layer

3. Activation Layer:

The activation layer applies a nonlinear activation function, such as the ReLU function, to the output of the pooling layer, which helps introduce nonlinearity into the model so that it can learn more complex representations of the input data.

Figure 5: Activating the layer

Now that you've mastered the basics of convolutional and pooling layers, you've learned the basics of CNNs

4. Normalization Layer:

Normalization layers perform normalization operations, such as batch normalization or layer normalization, to ensure that each layer is activated in good condition and to prevent overfitting.

Figure 6: Different types of normalization plots

5.Dropout 层：

The dropout layer is used to prevent overfitting by randomly discarding neurons during training. This helps ensure that the model doesn't remember the training data, but instead generalizes to new, unseen data.

Figure 7: How does the Dropout layer work?

6. Dense layer:

After the convolutional and pooling layers extract features from the input image, dense layers can be used to combine these features and make final predictions. In CNNs, the dense layer is usually the last layer and is used to generate output predictions. The activations of the first few layers are flattened and passed as inputs to the dense layer, which performs the weighted sum of the inputs and applies the activation function to produce the final output.

Figure 8: Dense layer

Benefits and advantages of convolutional neural networks

1. Feature extraction: CNNs are able to automatically extract relevant features from the input image, reducing the need for manual feature engineering.

1. Spatial invariance: CNNs can recognize objects in an image, regardless of their position, size, or orientation, which makes them ideal for object recognition tasks.

1. Noise Immunity Robustness: CNNs can often handle noisy or cluttered images, which makes them useful for practical applications where image quality may vary.

1. Transfer learning: CNNs can take advantage of pre-trained models, reducing the amount of data and computational resources required to train new models.
2. Performance: CNNs demonstrate state-of-the-art performance on a range of computer vision tasks, including image classification, object detection, and semantic segmentation.

Limitations of Convolutional Neural Networks:

1. Computational cost: Training a deep CNN can be computationally expensive and requires a lot of data and compute resources.
2. Overfitting: Deep CNNs are prone to overfitting, especially when training on small datasets, where the model may remember the training data instead of generalizing to new, unseen data.
3. Lack of explainability: CNNs are considered "black box" models, making it difficult to understand why a particular prediction is made.
4. Limited to mesh-like structures: CNNs are limited to mesh-like structures and cannot handle irregularly shaped or non-mesh-like data structures.

Conclusion:

In conclusion, convolutional neural networks (CNNs) are a powerful deep learning architecture that is well suited for image classification and object recognition tasks.

With the ability to automatically extract relevant features, process noisy images, and leverage pre-trained models, CNNs demonstrate state-of-the-art performance across a range of computer vision tasks.

However, they also have their limitations, including high computational costs, overfitting, lack of interpretability, and limited ability to handle irregular shapes.

Still, CNNs are a popular choice for many computer vision tasks.

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