Why we use bias in neural networks? Why do we scale our data in neural networks?

Question

Accepted Answer

Bias in neural networks helps in capturing the underlying patterns in data. Scaling data helps in improving convergence and performance.

Bias in neural networks helps in shifting the activation function to better fit the data.
It allows the model to capture the underlying patterns in the data by providing flexibility in the decision boundary.
Scaling data helps in improving convergence by ensuring that the gradients are of similar scale.
It also helps in improving the performance of the model by preventing certain features from dominating the learning process.
For example, in image classification tasks, bias helps in capturing variations in lighting conditions, while scaling data ensures that pixel values are within a similar range.

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