Neural networks are machine learning models that mimic the human brain, processing data through interconnected layers (input, hidden, output). They learn from patterns to recognize images, understand speech, and make predictions. From the 1950s Perceptron to today’s deep learning, they form the backbone of modern AI, reducing human intervention effectively.
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Neural networks, or artificial neural networks (ANNs), are a subset of machine learning that mimic the way the human brain processes information.
It AI systems to recognize patterns, classify data, and make predictions without much human intervention.
Neural networks learn from data. They recognise patterns and improve their accuracy. This adaptability makes them ideal for tasks like image recognition, speech processing, and fraud detection.
They work by processing input data through 3 interconnected layers of artificial neurons.
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The concept of neural networks dates back to the 1950s when Frank Rosenblatt developed the Perceptron, one of the earliest AI models. Over time, perceptrons evolved into multi-layer neural networks, which lead to deep learning models. |
Neural networks are the backbone of modern AI. They enable machines to analyze complex data, recognize speech, detect objects in images, and even understand human language.
They help AI systems learn and adapt, reducing the need for human intervention. For example, AI-powered chatbots, self-driving cars, medical diagnosis systems, and voice assistants (like Siri and Alexa) depend on neural networks to function effectively.
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PRACTICE QUESTION Q. "Artificial Intelligence has the potential to transform society, but raises significant ethical concerns." Critically analyze. 150 words |
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