Why Feature scaling is important for neural networks because?

 1. Gradient Descent Convergence: Features with larger scales can dominate the gradient calculation,

    leading to slower convergence and potentially getting stuck in local minima. Scaling brings all

    features to a similar range, allowing the optimization algorithm to find the minimum more efficiently.

 2. Activation Functions: Many activation functions (like sigmoid or tanh) are sensitive to the input

    range. Large input values can lead to saturation, where the gradient becomes very small, hindering

    learning. Scaling prevents this saturation by keeping inputs within a reasonable range.

 3. Weight Initialization: Proper weight initialization techniques assume that input features are scaled.

    If features have vastly different scales, the initial weights might not be appropriate, leading

    to instability during training.

 4. Regularization Techniques: Some regularization techniques (like L2 regularization) penalize large

    weights. If features are not scaled, the model might be forced to assign large weights to features

    with larger scales, disproportionately affecting the regularization penalty.

One more aspect of this is below 

Why Feature Scaling is Important

Faster Convergence

Neural networks optimize using gradient descent.

If features are on different scales, gradients can oscillate and take longer to converge.

Avoids Exploding/Vanishing Gradients

Large feature values can lead to exploding gradients

Very small feature values can lead to vanishing gradients.

Better Weight Initialization

Neural networks assume inputs are centered around 0 (especially with activations like tanh or ReLU).

If features vary drastically, some neurons may become ineffective (e.g., stuck ReLUs).

Equal Contribution from Features

Without scaling, features with larger ranges dominate the loss function and bias the model unfairly.