Covers: theory of Normalization

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

In the first module, we are going to learn about normalizing inputs in a neural network. this concept is a component of batch normalization. Andrew explains this concept with a variety of examples.

Fail to play? Open the link directly: https://www.youtube.com/watch?v=FDCfw-YqWTE

Amir Parizi**Total time needed: **~3 hours

- Learning 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
- Target Audience
- INTERMEDIATE

Go through the following **annotated items** *in order*:

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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