Covers: implementation of Forward propagation

- How to implement FP in python?

In this module, we are going to read this code in python which is an implementation of Forward Propagation. Normally, we are not going to write these kinds of codes from the scratch in the projects. However, reading the code once would give you a better idea of what is going to happen when we call Forward Propagation from different packages in Python this is an exercise from a mini-course in Datacamp. you can watch the video before this exercise if you want

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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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