Normalization adjusts input data to improve model performance and training speed.
Normalization in Deep Learning
It ensures features contribute equally, preventing bias towards larger scale variables.
Why Normalize Data?
Popular methods include Min-Max Scaling, Z-score Normalization, and Batch Normalization.
Common Techniques
Scales data to a fixed range (0-1), preserving the original distribution but sensitive to outliers.
Min-Max Scaling
Standardizes data to have a mean of 0 and standard deviation of 1, making it Gaussian-like.
Z-score Normalization
Normalizes inputs within each mini-batch, enhancing training stability and speed.
Experiment with different techniques for optimal model performance based on your dataset.
Choosing the Right Method