Covers: theory of Batch normalization

- How to fit batch norm into neural network?

In this module, Andrew explains the math behind batch norm and how to fit this concept into the neural network system.

Fail to play? Open the link directly: https://www.youtube.com/watch?v=em6dfRxYkYU&t=8s

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- Objectives
- Learn about Batch normalization concept and math behind it
- Potential Use Cases
- Batch normalization is a technique for training very deep neural networks that standardizes the inputs to a layer for each mini-batch
- Who is this for ?
- INTERMEDIATE

Click on each of the following **annotated items** to see details.

VIDEO 1. Normalizing Inputs

- How does normalization work?
- why do we need to normalize our inputs in a neural network?

10 minutes

ARTICLE 2. Intro on mini batch gradient descent (with pseudo code)

- What is Mini-Batch Gradient Descent?
- How to Configure Mini-Batch Gradient Descent?

20 minutes

LIBRARY 3. Mini-batch GD from scratch in Python

- How to implement Mini-batch GD in python?

10 minutes

ARTICLE 4. Forward propagation in neural networks

- What is Forward propagation?
- what is the math behind this concept?

20 minutes

LIBRARY 5. Forward propagation from scratch in Python

- How to implement FP in python?

20 minutes

VIDEO 6. Why Does Batch Norm Work? [no math!]

- What is Batch normalization?
- Why Does Batch Norm Work?

15 minutes

VIDEO 7. Fitting Batch Norm Into Neural Networks [ more advanced math here! ]

- How to fit batch norm into neural network?

13 minutes

LIBRARY 8. How to implement Batch Normalization(BN) using Python from scratch

- How to implement Batch Normalization In Neural Networks using Python?

20 minutes

LIBRARY 9. Batch normalization in Keras

- how to implement batch normalization in Keras?

20 minutes

PAPER 10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (OPTIONAL)

- Where does this method come from?

30 minutes

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