3 Questions
What type of learning does not provide any information about the output?
Unsupervised learning
What type of learning uses example inputs to train the neural network?
Supervised learning
What is the hidden layer made up of?
Interconnected nodes
Study Notes
- Neural networks are a type of machine learning that contain an input layer, output layer and at least one hidden layer.
- The hidden layer is made up of several interconnected nodes and these nodes have an inbuilt ‘activation function’.
- Neural networks use three ways of learning – supervised, unsupervised and reinforcement learning.
- Supervised learning uses example inputs to train the neural network to provide correct outputs. Unsupervised learning does not provide any information about the output, and is used for solving clustering problems, estimation problems and self-organising maps. Reinforcement learning uses feedback to train the neural network.
Test your knowledge of neural networks, their structure and learning methods in machine learning. Explore the concepts of supervised, unsupervised, and reinforcement learning in neural networks.
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