*The above article contains the formulas with minimal explanations. If you have seen this topic some time ago, it is probably a good way to refresh memories. However, this would be a bit too harsh to start from scratch with it. …*

In the precedent article, I spoke about the creation of a Bag of Words in R. Then, I made tests on two different datasets. This is a good occasion to tell a little about some commonly used classification algorithms.

I will give some code samples in R, but this is…

**Bag of Word** embedding is a Natural Language Processing technic to embed sentences into a fixed-size numeric vector. The goal is to use this vector as an input for a machine learning algorithm.

Bag of Words is simple to understand an is a great technic when you want to keep…

A big part of the above formulas are from my notes at the DSTI. I also added curves I found or created, as well as more gradient descent algorithms. Please note that it is a formulas cheat sheet, not a course. It is good to check or refresh your knowledge.

Because it is sometime useful…

**Super/sub script:**

- Numerical exponents: ⁰ ¹ ² ³ ⁴ ⁵ ⁶ ⁷ ⁸ ⁹
- Numerical indices: ₀ ₁ ₂ ₃ ₄ ₅ ₆ ₇ ₈ ₉
- Superscript : ᵃ ᵇ ᶜ ᵈ ᵉ ᶠ ᵍ ʰ ⁱ ʲ ᵏ ˡ ᵐ ⁿ ᵒ ᵖ ʳ…

I’m always very surprised when I hear this…

- “Julia is not for beginners.”
- “Julia is for people who make complex numeric calculus”
- “I have friends who use Julia, they are all very smart people”.

And ear this on a regular basis… But why the hell?

I learned both Python and…

This is a cheat-sheet for descriptive statistics and probability with some R. It is for a big part from my notes of the DSTI courses, while some concepts are from other courses. It starts with the very basics and will cover more advanced features over time.

On time to time…

This one is a cheat-sheet for pretty general formulas of calculus such as derivatives, integrales, trigonometry, complex numbers… Something you may find useful in many contexts. It is also a good way to check what you remember years after school… ¯\_(ツ)_/¯

`x <- rgamma(1000,1)`

hist(x, c(0,1,3,10))

The above histogram is an alternative way to plot the probability density function of the gamma distribution, on a sample of 1000 items.

Easy to plot, thanks to the “hist” function, right ? But how does it work ?

Here is the position of each…