Explanation
Think of the human brain, a complex web of interconnected neurons. Neural networks are inspired by this very structure. They are a set of algorithms, interconnected like neurons, that are designed to recognise patterns.
Each 'neuron' in the network receives inputs, processes them, and passes the result on to other neurons. The strength of these connections is adjusted during a training process, allowing the network to 'learn' from data.
Imagine showing the network thousands of pictures of cats and dogs. Over time, it adjusts the connections between its neurons until it can accurately distinguish between the two animals.
This learning process allows neural networks to perform complex tasks such as image recognition, natural language processing, and predictive modelling.
Examples
Consumer Example
Consider the voice assistant on your smart speaker. Neural networks power its ability to understand your spoken commands and respond accordingly.
They analyse your voice, identify the words you are saying, and determine the intent behind your request.
It's like having a digital butler who understands your every command.
Business Example
Imagine a bank using neural networks to detect fraudulent transactions.
The network analyses transaction data, looking for patterns that are indicative of fraud.
For instance, a sudden large transaction from an unusual location could trigger a red flag.
It's like having a vigilant security guard who is always on the lookout for suspicious activity. This helps the bank prevent losses and protect its customers.