Neural Network Foundations: Activation Functions and Topology
Design basic neural network architectures and understand how activation functions enable nonlinear learning.
Neural networks mimic biological neurons through interconnected layers of processing units. Each neuron applies an activation function to weighted inputs plus bias. Activation functions (sigmoid, ReLU, tanh) introduce nonlinearity, allowing networks to learn complex patterns beyond linear regression. Network topology defines the number of hidden layers and neurons per layer; deeper networks capture hierarchical patterns but risk overfitting. Forward propagation passes data through layers; backpropagation adjusts weights using gradient descent to minimize error. Understanding weight initialization, learning rate, and convergence criteria is essential. ReLU (Rectified Linear Unit) is popular in modern networks for efficiency, while sigmoid suits binary classification output…
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