Learn how backpropagation adjusts weights in a neural network. Discover why it propagates weight changes backward from sink to source, optimizing model accuracy. Backpropagation is a fundamental algorithm in neural network training, specifically designed to minimize the error (or loss) in predictions by iteratively updating the weights of the network.
Table of Contents
Question
Backpropagation is a learning technique that adjusts weights in the neural network by propagating weight changes
A. Backward from sink to source
B. Forward from source to sink
C. Backward from sink to hidden nodes
D. Forward from source to hidden nodes
Answer
A. Backward from sink to source
Explanation
Backpropagation, short for backward propagation of errors, is a supervised learning technique that optimizes a neural network’s weights and biases by propagating the error gradient backward through the network. Here’s how it works:
- Forward Pass: The input data flows through the network from the input layer (source) to the output layer (sink), producing predictions.
- Error Calculation: The difference between the predicted output and the actual output is computed using a loss function.
- Backward Pass: Using the chain rule of calculus, the error gradient is propagated backward from the output layer (sink) to earlier layers (source). This step calculates how much each weight contributes to the overall error.
- Weight Updates: Optimization algorithms like gradient descent use these gradients to adjust weights and biases, reducing future errors.
This backward propagation ensures that all layers in the network contribute to minimizing the loss function effectively.
Why “Backward from Sink to Source”?
The term “sink” refers to the output layer where predictions are made, while “source” refers to earlier layers closer to the input. During backpropagation:
- The algorithm starts at the output layer (sink), computes gradients with respect to the loss, and moves backward through each preceding layer.
- This process ensures that all weights in every layer are updated based on their contribution to the final error.
By propagating changes backward, backpropagation efficiently optimizes even deep neural networks with multiple layers, making it a cornerstone of modern machine learning techniques like convolutional neural networks (CNNs)310.
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