What are neural networks, and how do they function?

Neural networks are a subset of artificial intelligence modeled after the human brain’s structure, designed to recognize patterns and solve complex problems. They consist of layers of interconnected nodes (or neurons), which process data through weighted connections.

Structure and Components

1. Input Layer: Receives the raw data or features. Each node represents one input feature.
   
2. Hidden Layers: Perform computations by transforming inputs through weights, biases, and activation functions. Multiple hidden layers create a "deep" neural network.
   
3. Output Layer: Produces the final prediction or classification based on the processed data.
   

How They Function

1. Data Processing: Inputs are passed into the network, where each connection has a weight that determines the influence of the input on the output.
   
2. Weighted Sum: Each neuron calculates a weighted sum of its inputs and applies a bias term.
   
3. Activation Function: Non-linear functions like ReLU, sigmoid, or tanh are applied to introduce non-linearity, enabling the network to learn complex patterns.
   
4. Forward Propagation: The processed data flows through the layers, and the network generates an output.
   
5. Loss Calculation: The difference between the predicted output and actual value is measured using a loss function.
   
6. Backpropagation: The network adjusts weights and biases by propagating errors backward, using algorithms like gradient descent to minimize the loss.
   
7. Iteration: The process repeats over multiple epochs until the model learns effectively.
   

Applications

Neural networks power advanced systems such as image recognition, natural language processing, autonomous vehicles, and recommendation engines. They excel at finding hidden relationships in complex and high-dimensional data.