The Surprising Science Behind Neural Networks: How They Work and How to Build Your Own (Beginner’s Guide)
Neural networks use layers of connected neurons, each with adjustable weights and biases, to learn patterns from data. Training involves forward passes, loss calculation, and backpropagation to update parameters. Activation functions like sigmoid and ReLU allow modeling of complex relationships. Most networks are feedforward, with training requiring large datasets and multiple epochs.