A feedforward network is the simplest neural architecture: data flows in one direction from input to output with no cycles. But knowing the architecture is only half the problem — the network must learn, meaning it must adjust weights so predictions improve over time. Backpropagation, published in its modern form by Rumelhart, Hinton, and Williams in 1986, makes this possible. Without backpropagation, training a multi-layer network would require computing gradients manually for every weight — infeasible for millions of parameters. Backpropagation applies the chain rule of calculus systematically: it computes how much each weight contributed to the final error, then nudges each weight in the direction that reduces that error. Understanding backpropagation is not optional — it is the engine of every neural network, and debugging training failures requires knowing what it computes and where it breaks.
35 minadvanced
Feedforward networks and backpropagation
Analogy🏏Cricket
🏏 Think of it like cricket: The Duckworth-Lewis-Stern (DLS) method, the ICC bowling economy metric, and the net run rate each measure team performance but optimise for completely different things — DLS cares about wickets-in-hand, economy cares about runs-per-over, NRR cares about tournament-wide margins. Using the wrong metric to evaluate a bowler is like using MSE for classification: technically computable but optimising the wrong thing entirely. Just as you would never rank a spinner by batting average, you should never use MSE when your model predicts probabilities. Just as focal DLS adjustments down-weight easy chases and amplify close finishes, focal loss down-weights easy examples and amplifies hard minority-class examples. The choice of scoring metric defines what excellence means — and so does the choice of loss function.
Lesson 2 of 35
0% complete