Discover how neural networks minimize prediction errors during training by adjusting weights between neurons. Learn the role of backpropagation and gradient descent in optimizing neural network performance.
Table of Contents
Question
During training, what do neural networks adjust to minimize the difference between predicted output and actual output?
A. Activation Function type
B. Input data structure
C. Weights between neurons
D. Number of Hidden Layers
Answer
C. Weights between neurons
Explanation
During training, neural networks adjust the weights between neurons to minimize the difference between the predicted output and the actual target values. This process is central to how neural networks learn and improve their predictions.
Why Weights Are Adjusted
Weights determine the strength of connections between neurons in a neural network. By modifying these weights, the network can change how it processes inputs, allowing it to better approximate the desired outputs. The goal is to iteratively reduce the error (or loss) between the predicted and actual outputs.
How Weights Are Adjusted
- Forward Propagation: The input data is passed through the network, layer by layer, to compute a predicted output.
- Loss Calculation: The error between the predicted output and the actual output is calculated using a loss function (e.g., mean squared error or cross-entropy loss).
- Backpropagation: The error is propagated backward through the network using the chain rule of calculus. This calculates gradients—partial derivatives of the loss function with respect to each weight.
- Gradient Descent Optimization: Using these gradients, weights are updated in small steps (determined by a learning rate) in the direction that reduces the loss function. This iterative process continues until the loss is minimized or a stopping criterion (e.g., number of epochs) is met.
Key Algorithms Involved
- Backpropagation: Ensures that each weight adjustment is proportional to its contribution to the overall error.
- Gradient Descent: Guides weight updates toward minimizing the loss function efficiently.
Why Other Options Are Incorrect
A. Activation Function Type: The activation function determines how neuron outputs are transformed but remains fixed during training.
B. Input Data Structure: Input data structure is predefined and not altered during training.
D. Number of Hidden Layers: The architecture (including hidden layers) is fixed before training begins and does not change dynamically.
By adjusting weights iteratively, neural networks learn complex patterns in data, enabling them to make accurate predictions across various tasks like classification, regression, and more.
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