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Computing with Neurons

Artificial Neural Networks (ANNs) are the core mathematical frameworks that drive all modern AI. Inspired by the human brain, they consist of layers of interconnected "neurons" that pass signals based on weights and activation functions.

1. Forward Propagation

Data enters the input layer, gets multiplied by weights, and passes through an activation function (like ReLU) to decide if the neuron should "fire." This cascading signal is what generates the final prediction or output.

2. The Magic of Backpropagation

How does a network learn? By calculating the error (the loss) and using calculus (Gradient Descent) to nudge every single weight in the network in the direction that reduces that error. This process is repeated millions of times during training.

HISTORICAL CONTEXT: The concept of neural networks dates back to the 1940s, but it wasn't until the rise of GPUs and massive data in the 2010s that they became practically useful.

3. Architecture Diversity

From simple Feed-Forward networks to Recurrent Neural Networks (RNNs) for sequences, the topology of the connections defines what the AI is capable of learning.