Neural networks mimic the human brain to solve complex problems from "summary" of Machine Learning by Ethem Alpaydin
Neural networks are computational models inspired by the structure and function of the human brain. They consist of interconnected nodes, known as neurons, which process and transmit information through weighted connections. These connections are adjusted through a learning process based on input data, allowing neural networks to recognize patterns, make predictions, and solve complex problems.
The parallel between neural networks and the human brain lies in their ability to learn from experience. Just as our brains adapt and improve over time through exposure to new information, neural networks can adjust their connections to improve performance on tasks such as image recognition, speech processing, and natural language understanding. By mimicking the brain's capacity for learning and adaptation, neural networks have become a powerful tool for solving a wide range of complex problems in fields like computer vision, speech recognition, and robotics.
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