IB Digital Society Topic 3.6C Uses of Artificial Neural Networks Revision Notes (SL/HL)

IB Digital Society Topic 3.6C Uses of Artificial Neural Networks Revision Notes (SL/HL) | RevisionDojo

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3.6C Uses of Artificial Neural Networks

What an artificial neural network is

An artificial neural network is the structure behind deep learning. Loosely inspired by the brain, it is made of simple units called neurons arranged in connected layers that pass signals forward.
It has three kinds of layer: an input layer that receives data, one or more hidden layers that process, and an output layer that gives the answer. Information spreads across many small connections, not one rule.
This lets networks handle messy, high-dimensional inputs (images, sound, language) no human could hand-code. The cost is interpretability. Intelligence spread across millions of connections is hard to trace.

Inside the network: neurons and weights

A single neuron takes several numbers as inputs, multiplies each by a weight, adds them, and passes on a signal only if the total is high enough. The weight decides how much each input matters.
Weights are the whole story of what a network knows: a large weight strongly influences the next neuron, a small one barely matters. A network can hold millions of weights that together encode the patterns it picked up.
No human sets the weights; they start random and adjust automatically during training. The knowledge is a grid of numbers, not readable instructions, which is a major reason networks are a black box: even builders cannot say exactly why it decided as it did.

How a neural network learns

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3.6C Uses of Artificial Neural Networks

What an artificial neural network is

An artificial neural network is the structure behind deep learning. Loosely inspired by the brain, it is made of simple units called neurons arranged in connected layers that pass signals forward.
It has three kinds of layer: an input layer that receives data, one or more hidden layers that process, and an output layer that gives the answer. Information spreads across many small connections, not one rule.
This lets networks handle messy, high-dimensional inputs (images, sound, language) no human could hand-code. The cost is interpretability. Intelligence spread across millions of connections is hard to trace.

Inside the network: neurons and weights

A single neuron takes several numbers as inputs, multiplies each by a weight, adds them, and passes on a signal only if the total is high enough. The weight decides how much each input matters.
Weights are the whole story of what a network knows: a large weight strongly influences the next neuron, a small one barely matters. A network can hold millions of weights that together encode the patterns it picked up.
No human sets the weights; they start random and adjust automatically during training. The knowledge is a grid of numbers, not readable instructions, which is a major reason networks are a black box: even builders cannot say exactly why it decided as it did.

How a neural network learns

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Excepteur sint occaecat cupidatat non proident

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Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.
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Topic 1 - Introduction - Digital Society1 subtopic

Topic 2 - Concepts - Digital Society7 subtopics

Topic 3 - Content - Digital Society7 subtopics

Topic 4 - Contexts - Digital Society7 subtopics

Topic 5 - Challenges and Interventions - Digital Society (HL)3 subtopics

IB Digital Society 3.6C Uses of Artificial Neural Networks Notes

Topic 1 - Introduction - Digital Society1 subtopic

Topic 2 - Concepts - Digital Society7 subtopics

Topic 3 - Content - Digital Society7 subtopics

Topic 4 - Contexts - Digital Society7 subtopics

Topic 5 - Challenges and Interventions - Digital Society (HL)3 subtopics

3.6C Uses of Artificial Neural Networks

What an artificial neural network is

Inside the network: neurons and weights

How a neural network learns

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

3.6C Uses of Artificial Neural Networks

What an artificial neural network is

Inside the network: neurons and weights

How a neural network learns

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