Covers: theory of Cumulative Distribution function (CDF)
Estimated time needed to finish: 15 minutes
##### Why this is worth your time
Provides an overview of common discrete and continuous probability distributions

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### Probability basics for DeepLearning - Random Variables(RV)

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Total time needed: ~2 hours
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.
ARTICLE 1. What is a Random Variable
Gives a good description of Random variables in general sense
6 minutes
VIDEO 2. Types of Random Variables
Describes discrete and continuous Random Variables
30 minutes
ARTICLE 3. Probability Mass function (PMF)
Gives an idea of what does Probability Mass Function signifies for discrete RV
6 minutes
BOOK_CHAPTER 4. Relevance of Probability Mass function in Deep Learning
It explains Probability Mass function as relevant to Deep learning understanding
5 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 Function
15 minutes
BOOK_CHAPTER 6. Relevance of Probability Density Function in Deep Learning
It explains Probability Density Function as relevant to Deep learning understanding
10 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 Function
20 minutes
ARTICLE 8. Different types of probability distributions
Provides an overview of common discrete and continuous probability distributions
15 minutes

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