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Backprobagation the non mathematical approach.
- Backpropagation is a method that helps a neural network retrace its steps, figure out its mistakes, and improve over time.
- At the core of backpropagation lies a fundamental mathematical tool — derivatives.
- In calculus, a derivative measures how a function changes as its input changes.
- In machine learning, we use derivatives to measure how much a small change in a weight affects the overall error.
- Backpropagation helps a neural network “feel” whether it’s getting closer to the best solution or moving in the wrong direction.
- When a neural network makes a wrong prediction, we need to figure out which parts of the network contributed most to the error. Backpropagation computes the gradient, which tells us how to adjust each weight.
- The forward pass consists of data moving from the input layer through hidden layers to produce an output.
- In the backward pass, the error from the final layer is propagated backward, updating each weight based on how much it contributed to the mistake.
- Gradient descent is used to update the weights in the best direction to minimize the loss.
- Backpropagation makes deep learning possible by allowing neural networks to train on massive datasets and adjust millions of parameters efficiently.
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