The Surprising Science Behind Neural Networks: How They Work and How to Build Your Own (Beginner’s Guide)
Neural networks are brain-inspired models that learn from data by adjusting weights and biases, not by hand-coding every rule. They consist of layers—input, one or more hidden layers, and an output layer—where each neuron connects to the next layer. Each connection has a weight and each neuron includes a bias, and these are the learnable parameters adjusted during training. Activation functions such as sigmoid, ReLU, and tanh introduce non-linearity enabling the network to model complex patterns. A forward pass moves data from the input layer through hidden layers to the output, computing weighted sums, adding biases, and applying activations. Training
