Neural Networks & Deep Learning
What neural networks & deep learning means
Perceptrons, activation functions, backpropagation and deep architectures.
Before you start
Not confident yet? Work through Machine Learning Fundamentals first.
Worked example
One question from this skill's own question bank, shown with its full working.
An artificial neuron computes its output by:
- Multiplying all inputs together only
- Taking a weighted sum of inputs plus a bias, then applying an activation function
- Selecting the largest input
- Ignoring its inputs
Working: Each neuron computes activation(w·x + b); the weights and bias are what training adjusts.
Curriculum and exam alignment
Curriculum notes are a general guide to where this work normally sits. Always check the syllabus your school or examination body is currently using.
Related skills
Other Artificial Intelligence skills in Computing, at the same point in the course.
Other Computing topics
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Exams that assess neural networks & deep learning
University work on this skill feeds directly into these exam papers. Each page lists the Computing topics that appear on it.
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Practise neural networks & deep learning
8 questions with a worked answer after every attempt, and a SmartScore that tracks how close you are to mastery.