Neural Networks: A Model of the Human Brain

Mimicking the Human Brain
• Neural networks mimic the human brain’s neurons, processing and transmitting information.
• Synapses in neural networks are similar to the weights in the brain, determining the strength and importance of input signals.
• Both brain and artificial neurons activate based on received signals, adjusting weights during training using algorithms like backpropagation.

Main Components of Neural Networks
• Input Layer: Nodes receive input data, representing a feature or attribute.
• Hidden Layers: Process inputs, applying weights and activation functions.
• Output Layer: Provides the final output of the network, varying based on the task type.
• Weights and Biases: Parameters that transform input data within each node.
• Activation Functions: Introduce non-linearity into the model, enabling it to learn complex patterns.
• Loss Function: Measures the difference between predicted and actual output.
• Optimizer: An algorithm that adjusts weights and biases to minimize the loss function.

Training Process
• Forward Propagation: Inputs are passed through the network layer by layer to produce an output.
• Loss Calculation: Calculates the error between predicted and actual output.
• Backpropagation: Adjusts weights and biases by propagating the error backward.
• Iteration: Repeats steps 1-3 for many iterations until satisfactory performance.