backpropagation : the secret sauce of deep learning

hey data folks !

backpropagation is the algorithm that makes deep learning possible. it’s how neural networks learn from their mistakes, adjusting themselves to get better over time. without it, ai wouldn’t be nearly as powerful.

how does it work ?

imagine you’re teaching a kid to throw a basketball. at first, they miss. so you tell them : “aim a little higher.” they try again, adjust, and gradually improve. backpropagation does something similar, but for neural networks.

what are the key steps of backpropagation ?

backpropagation happens in four essential phases. each step is crucial to make the network learn efficiently.

1. forward propagation (using weights, biases, activation function)

input data is passed through the network, layer by layer.
each neuron applies weights, biases, and an activation function.
the final layer produces an output (the network’s prediction).

2. comparison (using a loss calculation)

the network compares its prediction to the real answer (ground truth).
a loss…