Batch normalisation – trainable and non trainable – day 27

Demystifying Trainable and Non-Trainable Parameters in Batch Normalization Batch normalization (BN) is a powerful technique used in deep learning to stabilize and accelerate training. The core idea behind BN is to normalize the output of a previous layer by subtracting the batch mean and dividing by the batch standard deviation. This is expressed by the following general formula: \[\hat{x} = \frac{x – \mu_B}{\sqrt{\sigma_B^2 + \epsilon}}\]\[y = \gamma \hat{x} + \beta\] Where: Why This Formula is Helpful The normalization step ensures that the input to each layer has a consistent distribution, which addresses the problem of “internal covariate shift”—where the distribution...

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Batch normalisation part 2 – day 26

Introduction to Batch Normalization Batch normalization is a widely used technique in deep learning that significantly improves the performance and stability of neural networks. Introduced by Sergey Ioffe and Christian Szegedy in 2015, this technique addresses the issues of vanishing and exploding gradients that can occur during training, particularly in deep networks. Why Batch Normalization? In deep learning, as data propagates through the layers of a neural network, it can lead to shifts in the distribution of inputs to layers deeper in the network—a phenomenon known as internal covariate shift. This shift can cause issues such as vanishing gradients, where...

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