Covers: implementation of Probability Mass function(PMF)

It explains Probability Mass function as relevant to Deep learning understanding

Read section 3.3.1 to see how much of this concept is needed in Deep learning

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Contributors

- Objectives
- This list will provide you an idea of RV, their types, different distributions from which RVs are sampled etc.
- Potential Use Cases
- Mathematical foundations behind Deep Learning
- Who is This For ?
- INTERMEDIATE

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

Resources5/8

ARTICLE 1. What is a Random Variable

Gives a good description of Random variables in general sense6 minutes

VIDEO 2. Types of Random Variables

Describes discrete and continuous Random Variables30 minutes

ARTICLE 3. Probability Mass function (PMF)

Gives an idea of what does Probability Mass Function signifies for discrete RV6 minutes

BOOK_CHAPTER 4. Relevance of Probability Mass function in Deep Learning

It explains Probability Mass function as relevant to Deep learning understanding5 minutes

ARTICLE 5. Probability density function (PDF)

Explains that Probability Density Function is not same as probability function. and how is Probability Density Function different from Probability Mass Function15 minutes

BOOK_CHAPTER 6. Relevance of Probability Density Function in Deep Learning

It explains Probability Density Function as relevant to Deep learning understanding10 minutes

ARTICLE 7. Definition of Cumulative Distribution function

Explains what is Cumulative Distribution function and how is it related to Prob. Density Function/ Prob. Mass Function20 minutes

ARTICLE 8. Different types of probability distributions

Provides an overview of common discrete and continuous probability distributions15 minutes

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