Neural Network Intuition

Before diving into the math, it helps to build a clear intuition for what a neural network is actually doing when it learns - the whole process boils down to two repeating phases: forward propagation and learning from error.

Forward Propagation

Data flows from the input layer, through each hidden layer, to the output layer. At every neuron, the inputs are weighted, summed, passed through an activation function, and forwarded to the next layer - this produces the network's prediction.

Input -> [Weighted Sum + Activation] -> Hidden Layer -> [Weighted Sum + Activation] -> Output

Measuring Error

Once a prediction is made, it's compared against the actual label using a loss function - the bigger the gap between prediction and reality, the higher the loss.

Learning from Mistakes

The network then works backward from the output to adjust its weights so that the error shrinks the next time around - this backward adjustment process is called backpropagation, and it relies on Gradient Descent, which you'll learn shortly.

A Helpful Analogy

Think of it like adjusting a recipe - you taste the dish (make a prediction), compare it to how it should taste (calculate error), and tweak the ingredients (adjust weights) a little at a time until it tastes right.

A neural network doesn't learn in one shot - it repeats forward propagation and weight adjustment over many iterations (epochs), gradually improving its predictions each time.

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