How do neural networks work?
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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.
Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They are designed to recognize patterns and relationships in data through a process of learning.
Basic Structure
A neural network consists of layers of interconnected units called neurons:
Input Layer:
Receives raw data (e.g., images, text, numbers).
Hidden Layers (one or more):
Perform computations by applying weights and activation functions.
This is where pattern detection and feature learning happen.
Output Layer:
Produces the final prediction or classification result.
How It Works
Forward Propagation:
Data flows from input to output.
Each neuron calculates a weighted sum of inputs and applies an activation function (like ReLU or sigmoid) to decide its output.
Loss Calculation:
The network's output is compared to the actual target using a loss function (e.g., mean squared error for regression, cross-entropy for classification).
Backpropagation:
The network calculates how much each neuron contributed to the error.
It adjusts the weights using an optimization algorithm like gradient descent to reduce future errors.
Iteration (Training):
This process repeats over many examples (epochs) until the model learns to make accurate predictions.
Applications
Image and speech recognition
Natural language processing
Medical diagnostics
Fraud detection
Self-driving cars
Summary
Neural networks learn by adjusting internal weights based on input data and feedback from errors. Through many iterations, they can detect complex patterns and make intelligent predictions, powering many of today's AI systems.
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