Fitting Batch Norm Into Neural Networks (C2W3L05)  Summary and Q&A
TL;DR
Batch normalization is a technique used in deep learning to normalize the inputs of each layer, leading to faster and more stable training of neural networks.
Key Insights
 ðĨš Batch normalization involves normalizing the inputs of each layer in a neural network, leading to faster and more stable training.
 ðĪŠ The normalization is done by subtracting the mean and dividing by the standard deviation of the Z values.
 ð Batch normalization eliminates the need for bias parameters in each layer and replaces them with beta parameters that control the shift or bias terms.
 â The parameters of batch normalization, beta and gamma, are updated during optimization using various algorithms like gradient descent, Adam, RMSprop, or momentum.
 ð― Batch normalization is usually applied with minibatches during training to compute batchspecific mean and variance values.
 ðŦĨ Implementing batch normalization is often just a oneline addition in deep learning frameworks.
 ðŧ Batch normalization significantly reduces the internal covariate shift, allowing each layer to learn more independently.
Transcript
so you've seen the equations for how to infer and  norm for maybe a single hidden layer let's see how it fits in to the training of a deep network so let's figure of a neural network like this you've seen me say before that you can view each hidden unit as computing two things first it computes Z and then it applies the activation function to comp... Read More
Questions & Answers
Q: What is the purpose of batch normalization in deep learning?
Batch normalization is used to normalize the inputs of each layer in a neural network, reducing the internal covariate shift, and improving the training speed and stability of the network.
Q: How does batch normalization work?
Batch normalization involves normalizing the values of Z in each layer by subtracting the mean and dividing by the standard deviation. The normalized values, V, are then passed through the activation function to obtain A.
Q: When is batch normalization applied in the training process?
Batch normalization is usually applied with minibatches during training. It involves computing the mean and variance of the Z values within each minibatch and normalizing them accordingly.
Q: How are the parameters of batch normalization updated during optimization?
The parameters of batch normalization, beta and gamma, can be updated using various optimization algorithms such as gradient descent, Adam, RMSprop, or momentum. The updates are made based on the computed gradients of beta and gamma.
Summary & Key Takeaways

Batch normalization is a twostep computation performed on each hidden unit in a neural network, where Z is computed first, followed by the application of the activation function to compute A.

Batch normalization involves normalizing the values of Z by subtracting the mean and dividing by the standard deviation, resulting in normalized values of V. These normalized values are then passed through the activation function to obtain A.

By normalizing the inputs, batch normalization helps in reducing the internal covariate shift, allowing each layer to learn more independently and improving the training speed and stability of deep neural networks.