Deep Learning: Importance of Data Normalization

This article highlights how normalization mitigates the influence of scale, enhances convergence, enables efficient optimization, handles outliers, and promotes generalization and robustness.

Rahul S

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Normalizing data is an essential preprocessing step in neural networks. It involves scaling and transforming input data to have a consistent range and distribution.

Mitigating the influence of scale:

Neural networks are sensitive to the scale of input features. When features have significantly different scales, some weights and biases may dominate the learning process. Consequently, the network may converge slowly or get stuck in suboptimal solutions. Normalizing the data ensures that all features contribute equally by bringing them to a similar scale.

Consider a neural network for image recognition. If the pixel intensities range from 0 to 255, while the other features are in the range of 0.1 to 1, the network may assign more importance to pixel intensities due to their larger values. Normalizing the data will address this issue by scaling all features appropriately.

Improved convergence:

When input values are not normalized, large values can saturate these activation functions, causing small gradients during backpropagation. This “vanishing gradient problem” slows down learning or prevents convergence. Normalizing the data helps avoid activation function saturation by keeping input values within a reasonable range.

In natural language processing, if word embeddings have different scales, some words with large embeddings may dominate the learning process, leading to biased predictions. Normalizing the word embeddings can help prevent this bias. This is common in transformers.

Efficient optimization:

Optimization algorithms like stochastic gradient descent (SGD) assume that features are centered around zero with similar standard deviations. When data is not normalized, the optimization process becomes less efficient. Adjusting learning rates for different features individually…

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