CBSE Class 10th AI (Artificial Intelleigence) | 23. Convolutional Neural Network (CNN) by Abraham | Learn Smarter
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23. Convolutional Neural Network (CNN)

Convolutional Neural Networks (CNNs) are specialized deep learning models tailored for processing visual data such as images and videos. They automate the feature extraction process and address challenges presented by traditional neural networks, enhancing performance and efficiency. Widely applied in fields like face recognition and medical imaging, CNNs continue to evolve as a major component in AI-driven visual analysis.

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Sections

  • 23

    Convolutional Neural Network (Cnn)

    This section introduces Convolutional Neural Networks (CNNs), a specialized type of Deep Learning model that processes visual data such as images and videos.

    Learning Practice

  • 23.1

    Introduction

    This section introduces Convolutional Neural Networks (CNNs), explaining their role in recognizing visual data like images and videos.

    Learning Practice

  • 23.2

    What Is A Convolutional Neural Network (Cnn)?

    A Convolutional Neural Network (CNN) is an advanced type of AI neural network designed for analyzing visual data like images.

    Learning Practice

  • 23.3

    Why Use Cnn Instead Of Regular Neural Networks?

    CNNs are preferred over traditional neural networks for image processing due to their ability to maintain spatial relationships and reduce the complexity of model parameters.

    Learning Practice

  • 23.4

    Structure Of A Cnn

    This section outlines the various layers that make up a Convolutional Neural Network (CNN) and their respective functions.

    Learning Practice

  • 23.4.1

    Input Layer

    The input layer of a CNN represents the image as a matrix of pixels.

    Learning Practice

  • 23.4.2

    Convolutional Layer

    The convolutional layer applies filters to images, enabling CNNs to detect important visual features.

    Learning Practice

  • 23.4.3

    Activation Function (Relu)

    The ReLU activation function adds non-linearity to Convolutional Neural Networks by replacing negative values with zero, enabling the network to understand complex patterns.

    Learning Practice

  • 23.4.4

    Pooling Layer

    The pooling layer in a CNN reduces the size of the feature maps while retaining important information, which decreases computational load.

    Learning Practice

  • 23.4.5

    Fully Connected Layer (Fc)

    The Fully Connected Layer in CNNs is responsible for making final classifications based on features extracted from previous layers.

    Learning Practice

  • 23.5

    Applications Of Cnn

    CNNs are widely used in various real-life applications, enhancing tasks such as image recognition and object detection.

    Learning Practice

  • 23.6

    Advantages Of Cnn

    CNNs offer automatic feature extraction, efficiency, and high accuracy for visual tasks.

    Learning Practice

  • 23.7

    Limitations Of Cnn

    CNNs have limitations such as requiring large datasets, being computationally intensive, and the potential for overfitting during training.

    Learning Practice

  • 23.8

    Common Cnn Architectures

    This section introduces popular architectures of Convolutional Neural Networks (CNNs) and their purposes.

    Learning Practice

  • 23.9

    Cnn Vs Human Brain

    CNNs are modeled after the human brain's visual cortex, learning to recognize objects through training with images.

    Learning Practice

  • 23.10

    Summary

    CNNs are essential deep learning models used for image and video analysis, composed of several structured layers.

    Learning Practice

References

Chapter_23_Convo.pdf

Class Notes

Memorization

What we have learnt

  • CNNs are designed specifica...
  • They utilize a structured a...
  • Applications of CNNs range ...

Revision Tests