How Neural Networks Learn, Backpropagation
Backpropagation
is a fundamental algorithm for training neural networks.
It involves calculating the difference between the predicted value from the forward pass and the actual correct answer, and then adjusting weights
and biases
based on this error.
Through backpropagation, neural networks are able to learn to make increasingly accurate predictions.
Even if a neural network initially produces incorrect results, the iterative process of backpropagation helps it find the optimal weights over time.
Input: Handwritten digit image Forward Pass: Misclassified '8' as '3' Error Calculation: Calculate the difference between the correct answer (8) and the predicted value (3) Backpropagation: Adjust weights for more accurate predictions in subsequent learning
Backpropagation is the core algorithm for reducing prediction errors by adjusting weights, calculating error, and finding gradients for weights, which are then updated using gradient descent.
However, as layers deepen, the vanishing gradient problem can occur, which is why activation functions like ReLU are used to mitigate this issue.
In the next lesson, we'll take a simple quiz to review what we've learned so far.
신경망이 학습하는 과정에서 역전파 알고리즘의 주된 역할은 무엇인가요?
데이터를 전처리하는 역할
예측 값과 실제 값의 차이를 계산
가중치와 편향을 조정하여 예측 오차를 줄임
신경망의 구조를 설계
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