How do neural networks differ from traditional computing models?

Neural networks differ from traditional computing models by mimicking the human brain's structure and learning process.

Traditional computing models, also known as Von Neumann architecture, operate on a set of predefined instructions. They follow a sequential process where data is fetched from memory, instructions are decoded, and operations are executed. This model is deterministic, meaning the same input will always produce the same output. It's highly efficient for tasks with clear rules and procedures, but it struggles with complex, unstructured data and tasks requiring learning or adaptation.

On the other hand, neural networks, a subset of artificial intelligence, are designed to replicate the human brain's functionality. They consist of interconnected layers of nodes, or 'neurons', which can process and transmit information in parallel. Unlike traditional models, neural networks learn from experience. They adjust their internal parameters based on the input they receive and the error they make, a process known as 'training'. This allows them to improve their performance over time and adapt to new data.

Neural networks excel in tasks that are challenging for traditional models, such as image and speech recognition, natural language processing, and predictive analytics. They can handle unstructured data, learn from it, and make predictions or decisions based on patterns they've recognised. However, they require large amounts of data and computational power to train effectively, and their 'black box' nature can make it difficult to understand how they've arrived at a particular output.

In summary, while traditional computing models follow predefined instructions in a sequential manner, neural networks mimic the human brain's structure and learning process, allowing them to adapt and learn from experience. This makes them particularly suited to tasks involving complex, unstructured data and pattern recognition.