Understanding Batch Normalization and its Contribution to Deep Learning
Between Inception and Xception
In my last article we explored Inception architecture and in a future article I will explain Xception. However, before we proceed to Xception, we need to understand Batch Normalization and its importance in the context of Deep Learning, especially for CNN.
Batch Normalization
Batch Normalization is a technique used in Deep Learning to make neural networks more efficient. When we train a neural network, the weights of the model are adjusted based on input data to make accurate predictions. However, as a network trains, the distribution of the values in each layer’s output can change, making the training process more difficult.
Batch normalization addresses this problem by normalizing the output of each layer. Normalization means transforming the values so that they have a mean close to 0 and a standard deviation close to 1. The normalization formula is:
Mini-Batches
The normalization process is done within small groups of data, which we call “mini-batches”…
