🔹 Artificial Neurons (Nodes) – Basic computing units that process and transmit information.
🔹 Connections (Synapses) – Links between neurons, each with an associated weight that determines signal strength.
🔹 Activation Functions – Mathematical functions that decide which neurons get activated and contribute to the final prediction.
The more neurons and connections a neural net has, the more complex patterns it can learn:
- Higher accuracy – More neurons = better pattern recognition.
- Increased capacity – The network can store and recall more intricate relationships.
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