IB Computer Science Topic A4.3.8 Structure and Function of Anns Revision Notes (HL)

IB Computer Science Topic A4.3.8 Structure and Function of Anns Revision Notes (HL) | RevisionDojo

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What Are Artificial Neural Networks?

Artificial Neural Networks (ANNs) are computational models inspired by the human brain.
They consist of interconnected nodes or "neurons" that work together to process and learn from input data.

Note

ANNs are used for tasks like classification, regression, and pattern recognition.

Key Components of ANNs

Nodes (Neurons)

Role: Fundamental units of the network.
Each neuron:
1. Receives inputs.
2. Applies weights + bias.
3. Passes the result through an activation function.
Produces an output that feeds into the next layer.

Weights

Numerical values that determine the strength/importance of each input connection.
Adjusted during training to minimize errors.

Bias

A constant term added to the weighted sum.
Helps the network shift activation functions to better fit data.
Without bias, some patterns may be impossible to learn.

Activation Function

Decides whether a neuron “fires” and introduces non-linearity.
Common examples:
1. Sigmoid: Output between 0 and 1.
2. ReLU (Rectified Linear Unit): Outputs positive values or 0.
3. Tanh: Output between –1 and 1.
Without activation functions, ANNs would act like simple linear models.

Layers of an ANN

Input Layer: Receives raw data (features).
Hidden Layers: Perform computations and extract patterns.

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Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum.

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What Are Artificial Neural Networks?

Artificial Neural Networks (ANNs) are computational models inspired by the human brain.
They consist of interconnected nodes or "neurons" that work together to process and learn from input data.

Note

ANNs are used for tasks like classification, regression, and pattern recognition.

Key Components of ANNs

Nodes (Neurons)

Role: Fundamental units of the network.
Each neuron:
1. Receives inputs.
2. Applies weights + bias.
3. Passes the result through an activation function.
Produces an output that feeds into the next layer.

Weights

Numerical values that determine the strength/importance of each input connection.
Adjusted during training to minimize errors.

Bias

A constant term added to the weighted sum.
Helps the network shift activation functions to better fit data.
Without bias, some patterns may be impossible to learn.

Activation Function

Decides whether a neuron “fires” and introduces non-linearity.
Common examples:
1. Sigmoid: Output between 0 and 1.
2. ReLU (Rectified Linear Unit): Outputs positive values or 0.
3. Tanh: Output between –1 and 1.
Without activation functions, ANNs would act like simple linear models.

Layers of an ANN

Input Layer: Receives raw data (features).
Hidden Layers: Perform computations and extract patterns.

Definition

Paywall

(on a website) an arrangement whereby access is restricted to users who have paid to subscribe to the site.

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

Note

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam quis nostrud exercitation.

Excepteur sint occaecat cupidatat non proident

Tip

Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum.

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 A4.3.8: Structure and Function of Anns 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

What Are Artificial Neural Networks?

Key Components of ANNs

Nodes (Neurons)

Weights

Bias

Activation Function

Layers of an ANN

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

What Are Artificial Neural Networks?

Key Components of ANNs

Nodes (Neurons)

Weights

Bias

Activation Function

Layers of an ANN

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