Description: Input normalization is the process of scaling input data to a standard range to improve model performance. This process is crucial in the field of machine learning, as it allows algorithms to operate more efficiently and effectively. Normalization helps avoid issues such as numerical overflow and slow convergence during training. By standardizing the data, it ensures that each feature contributes equally to the loss function calculation, facilitating model optimization. There are different normalization techniques, such as min-max normalization, which scales data to a specific range, and Z-score normalization, which adjusts data to have a mean of zero and a standard deviation of one. These techniques are particularly relevant in neural networks, where input data can vary significantly in scale and distribution. Input normalization not only improves training speed but can also increase model accuracy, making it a fundamental step in data preprocessing for any machine learning task.
