Covers: theory of Adam

- Where does this optimizer come from?

This is the optional module for the students who want to learn more about Adam. This is the first article that introduces this concept.

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Contributors

- Objectives
- Learn the theory behind Adam as an optimizer and how to implement it in Python
- Potential Use Cases
- The algorithms leverages the power of adaptive learning rates methods to find individual learning rates for each parameter.
- Who is This For ?
- INTERMEDIATE

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

ARTICLE 1. Intro to mathematical optimization

- What is mathematical optimization?
- Why do we need to optimize a cost function in ML algorithms?

10 minutes

ARTICLE 2. RMSprop

- What is RMSprop?
- How does this algorithm work?

10 minutes

VIDEO 3. Gradient Descent with Momentum

- What is momentum in GD?
- How does momentum help optimizer to perform faster?

10 minutes

VIDEO 4. Adam optimization algorithm

- What is Adam optimizer?
- What is the math behind this optimizer and how does adam work?

10 minutes

ARTICLE 5. Adam optimizer [more advanced math concepts behind this algorithm]

- What is Adam optimizer?

25 minutes

LIBRARY 6. Implementing Adam optimizer in Python using Keras

- How to implement Adam optimizer in Python?

20 minutes

PAPER 7. ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION

- Where does this optimizer come from?

20 minutes

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