IB Computer Science Topic A4.3.9 Design of Cnns Revision Notes (HL)

IB Computer Science Topic A4.3.9 Design of Cnns Revision Notes (HL) | RevisionDojo

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How CNNs Learn Spatial Hierarchies of Features

Basic Architecture of CNNs

Input Layer: Receives the raw pixel data of an image.
Convolutional Layers: Apply filters (kernels) to detect features like edges and textures.
Activation Functions: Introduce non-linearity, often using ReLU (Rectified Linear Unit).
Pooling Layers: Reduce the spatial dimensions, preserving essential features.
Fully Connected Layers: Integrate features for classification.
Output Layer: Produces the final prediction, often using a softmax function for classification.

Note

This hierarchical structure allows CNNs to learn from simple to complex features, mimicking how humans recognize patterns in images.

Convolutional Layers: The Core of Feature Extraction

Convolution Operation: A filter slides over the input image, computing a dot product between the filter and the image patch.
Feature Maps: The output of the convolution operation, highlighting specific features detected by the filter.

Example

Consider a filter designed to detect vertical edges.
As it moves across the image, it produces high values where vertical edges are present, creating a feature map that highlights these edges.

Activation Functions: Introducing Non-Linearity

ReLU (Rectified Linear Unit): Sets negative values to zero, preserving positive values.
Purpose: Allows the network to learn complex, non-linear patterns.

Tip

ReLU is preferred in CNNs because it reduces the risk of the vanishing gradient problem, enabling faster and more effective training.

Pooling Layers: Reducing Dimensionality

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How CNNs Learn Spatial Hierarchies of Features

Basic Architecture of CNNs

Input Layer: Receives the raw pixel data of an image.
Convolutional Layers: Apply filters (kernels) to detect features like edges and textures.
Activation Functions: Introduce non-linearity, often using ReLU (Rectified Linear Unit).
Pooling Layers: Reduce the spatial dimensions, preserving essential features.
Fully Connected Layers: Integrate features for classification.
Output Layer: Produces the final prediction, often using a softmax function for classification.

Note

This hierarchical structure allows CNNs to learn from simple to complex features, mimicking how humans recognize patterns in images.

Convolutional Layers: The Core of Feature Extraction

Convolution Operation: A filter slides over the input image, computing a dot product between the filter and the image patch.
Feature Maps: The output of the convolution operation, highlighting specific features detected by the filter.

Example

Consider a filter designed to detect vertical edges.
As it moves across the image, it produces high values where vertical edges are present, creating a feature map that highlights these edges.

Activation Functions: Introducing Non-Linearity

ReLU (Rectified Linear Unit): Sets negative values to zero, preserving positive values.
Purpose: Allows the network to learn complex, non-linear patterns.

Tip

ReLU is preferred in CNNs because it reduces the risk of the vanishing gradient problem, enabling faster and more effective training.

Pooling Layers: Reducing Dimensionality

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Lorem ipsum dolor sit amet, consectetur adipiscing elit.
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A1 Computer fundamentals4 subtopics

A2 Networks4 subtopics

A3 Databases4 subtopics

A4 Machine learning4 subtopics

B1 Computational thinking1 subtopic

B2 Programming5 subtopics

B3 Object-oriented programming2 subtopics

B4 Abstract data types (HL only)1 subtopic

IB Computer Science Topic A4.3.9: Design of Cnns Notes

A1 Computer fundamentals4 subtopics

A2 Networks4 subtopics

A3 Databases4 subtopics

A4 Machine learning4 subtopics

B1 Computational thinking1 subtopic

B2 Programming5 subtopics

B3 Object-oriented programming2 subtopics

B4 Abstract data types (HL only)1 subtopic

How CNNs Learn Spatial Hierarchies of Features

Basic Architecture of CNNs

Convolutional Layers: The Core of Feature Extraction

Activation Functions: Introducing Non-Linearity

Pooling Layers: Reducing Dimensionality

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Excepteur sint occaecat cupidatat non proident

Unlock the rest of this chapter with a free account

How CNNs Learn Spatial Hierarchies of Features

Basic Architecture of CNNs

Convolutional Layers: The Core of Feature Extraction

Activation Functions: Introducing Non-Linearity

Pooling Layers: Reducing Dimensionality

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Excepteur sint occaecat cupidatat non proident

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