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Batch Normalization is a technique to improve the training of deep neural networks by normalizing the inputs to each layer, which helps in reducing internal covariate shift and accelerates convergence. It allows for higher learning rates, reduces sensitivity to initialization, and can act as a form of regularization to reduce overfitting.
Convergence speed refers to the rate at which an iterative algorithm approaches its solution, impacting the efficiency and feasibility of solving large-scale problems. Faster Convergence speeds are desirable as they reduce computational time and resources, making them crucial in practical applications such as optimization and machine learning.
Deep learning is a subset of machine learning that uses neural networks with many layers (deep neural networks) to model complex patterns in data. It has revolutionized fields such as image and speech recognition by efficiently processing large amounts of unstructured data.
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