Main Layers of a Neural Network

Input Layer: Receives the raw data (such as pixels in an image, words in a sentence, or numerical values) and passes it onward.
Hidden Layers: One or more intermediate layers where mathematical functions, weights, and activation functions process signals and extract features.
Output Layer: Produces the final result, prediction, classification, or decision.

How They Work (Brain Analogy)

Neurons vs. Nodes: Biological neurons communicate via electrical and chemical signals across synapses; artificial nodes pass numerical values through weighted connections.
Weights: Similar to how synapses strengthen or weaken in the brain, artificial connections have weights that scale the importance of a signal.
Training (Backpropagation): The network learns by comparing its output to the correct answer, measuring errors, and adjusting weights through backpropagation and gradient descent.

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Data Communication

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