Covers: theory of Mathematical Optimization

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

In this first module, we want to learn the basics of mathematical optimization and we want to know why do we need to optimize cost function in ML algorithms.

0 comment

Contributors

- Objectives
- Learn the theory behind AdaGrad as an optimizer and how to implement it in Python
- Potential Use Cases
- Adagrad is an algorithm for gradient-based optimization. it is well-suited when dealing with sparse data (NLP or image recognition).
- 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

VIDEO 2. Gradient Descent

- What is Gradient Decent(GD)?
- How does GD work in python?

10 minutes

ARTICLE 3. Learning Rate

- What is learning rate?
- How can I make it better?

20 minutes

ARTICLE 4. AdaGrad : Introduction (No math!)

- What is Adagrad?

10 minutes

ARTICLE 5. Adaptive Gradient (adaGrad) : Introduction [ With more advanced math concepts ]

- What is AdaGrad?
- What is the math behind this optimizer?

30 minutes

ARTICLE 6. AdaGrad in Python

- How to implement AdaGrad in Python

10 minutes

PAPER 7. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (optional)

- Where does this optimizer come from?

30 minutes

0 comment