Beginner level · Module 3 of 5 · 10 guided hours
Neural Networks and Generative AI Explained
Understand, without heavy mathematics, how neural networks work, how deep learning recognises images and speech, and how generative AI creates text and images.
Learning objectives
- Explain neural networks in plain language
- Describe what deep learning made possible
- Explain how chat assistants generate text and why they can be wrong
Topics
- artificial neurons and layers
- learning by adjusting weights
- deep learning for images and speech
- large language models
- next-word prediction
- hallucinations
Lessons
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Lesson 1 Neural Networks in Plain Language
Understand how layers of simple units, adjusted by training, can learn complex patterns.
artificial neuronweightslayerstraining adjustments
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Lesson 2 Deep Learning for Images and Speech
See what deep learning made possible in vision and speech, and its limits.
image recognitionspeech recognitionlarge data setslanguage and accent gaps
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Lesson 3 How Generative AI Creates Text
Understand how chat assistants generate text one piece at a time, and why fluent answers can be false.
large language modeltokensnext-word predictionhallucination
Practical activity
Explore how a chat assistant generates text: compare answers to the same question, ask about a local topic, and identify fluent but wrong answers.
Expected outcomes
- A plain-language understanding of neural networks and generative AI
- Healthy scepticism about fluent outputs
Assessment
Explain-it-back exercise: learners explain generative AI to a non-technical partner, plus a short quiz.
Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.