Neural networks are a type of artificial intelligence that helps computers learn from data, much like humans learn from experience. They are inspired by the human brain, which has millions of tiny cells called neurons that work together to process information.
In a neural network, there are layers of digital “neurons”:
Each connection between neurons has a weight, which tells the network how important that information is. The network “learns” by adjusting these weights to make better predictions over time.
Example: Neural networks are used in many things we see every day:
In simple words, a neural network is a computer system that learns from examples, finds patterns, and makes smart decisions, just like a tiny brain inside a machine.
Neural networks are a fundamental concept in Deep Learning, a subset of Artificial Intelligence that allows machines to learn from large amounts of data. They are inspired by the structure and function of the human brain, where neurons communicate through synapses to process information.
A neural network consists of layers of interconnected nodes called neurons:
During training, neural networks use backpropagation and optimization algorithms like gradient descent to adjust weights and biases, minimizing a loss function (difference between predicted and actual values).
Real-life Examples:
In short, neural networks allow machines to learn patterns from data, make decisions, and generalize to new, unseen information — mimicking human learning but at a much larger scale.
Documented by Nishu Kumari, Team edSlash.
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