What you see below is called "Sigma notation".

Something I've encountered frequently in the math for machine learning and number theory.

Here's how it works.
๐Ÿงต ๐Ÿ‘‡๐Ÿป
This weirdly shaped 'E' is the capital greek alphabet called 'Sigma' and it stands for summation.

Essentially what it allows us to do is represent equations where we add multiple items, let me show you what I mean.
Let's say we have to add squares of numbers from 1 to 10, you'd write it as an equation like this.

(The square of a number is basically multiplying the number by itself, like 2ยฒ, read as 2 squared = 2 x 2 =4)
You can write the same equation using Sigma notation.

Doesn't it look cleaner and shorter?
I like to think of Sigma as a 3 step process (like a 'for' loop if you're a programmer), you start from the bottom and then go towards the top.
In this case:

1. You start with i=1 (on the bottom)
2. Square it
3. Add 1 to the previous value of i
4. Square it
5. Add this to the previous squared number
6. Repeat until i reaches the number on the top which is 10 here.
At times you will notice a number in front of sigma, this basically multiplies with the entire equation with that particular number.
I first encountered sigma in the RSME (root mean squared error) formula, a commonly used metric while training machine learning models.
Before we bring this thread to end, try solving this sigma equation.

If you like bite-sized content on math, machine learning and programming consider following me.

Until next time, peace out.

โ€ข โ€ข โ€ข

Missing some Tweet in this thread? You can try to force a refresh
ใ€€

Keep Current with Pratham

Pratham Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @PrasoonPratham

13 May
This is a logarithm.

You'll see it in a lot of machine learning formulas, computer science, and math in general.

Here's a really simple explanation of how it works.
๐Ÿงต ๐Ÿ‘‡๐Ÿป
Before we understand how logarithms work, it is important to know how 'exponents' work.

aแถœ=b (read as "a to the power of c equals b") is an exponent where a,b and c are numbers.

aแถœ=b just means that a multiplied by itself c amount of times is equal to b.
Here are some examples:
- 2ยฒ basically means 2 multiplied by itself 2 times which is 4
- 2ยณ basically means 2 multiplied by itself 3 times which is 8

So on and so forth.
Read 11 tweets
7 May
Everyone tells you how important math is in machine learning.

But I believe that if you want to learn machine learning today, there are things more important than math which very few people will tell you about.

Here are 5 of them.

๐Ÿงต ๐Ÿ‘‡๐Ÿป
The field of machine learning has been very academic which is why there is so much emphasis on learning math for it.

But, today with the frameworks that we have, not knowing how to multiply matrices is far less of an issue than knowing how to use the tools in this list ๐Ÿ‘‡๐Ÿป
Yes, I know; you have R, Julia, and whatnot for machine learning but as of today, Python is the industry standard.

Get your data structures and algorithms right in Python because you will need them, here are some concepts worth learning ๐Ÿ‘‡๐Ÿป
Read 8 tweets
3 May
There are a million machine learning tutorials and courses out there which makes it really hard to choose the right one for you.

Here are some that I really like that you might find useful.
๐Ÿงต ๐Ÿ‘‡๐Ÿป
Before we begin, here's some unsolicited advice.

It is not about how many courses you take that matters but the problems that you solve with your knowledge.

You can take any combination of courses and tutorials from this list that you think will be useful.
Python crash course by Traversy Media (YouTube)

โ€ข Introduces you to a lot of basic concepts in Python
โ€ข Only 1.5 hours long
Read 14 tweets
2 May
Machine learning models can lie if you're not careful.

Here's how๐Ÿงต ๐Ÿ‘‡๐Ÿป
Not all machine learning models are the same but typically we look at 2
metrics for determining how good a model is.

They are (generally):
A. Accuracy : How accurate our model is (more the better)
B. Loss : How 'wrong' our model is (less the bettter)
In order to avoid overfitting, we split our data into 2 parts, one for training the model and the other for 'Validation'.

After every round of training the model,it is evaluated on the validation set.

This is done to ensure that the model does not memorize but learns instead.
Read 8 tweets
19 Apr
This is a step-by-step guide on how you can solve the Titanic disaster challenge on Kaggle.

๐Ÿงต ๐Ÿ‘‡๐Ÿป Image
Kaggle challenges are a great way to practice your machine learning skills.

In this thread, we'll go through each step for solving the beginner friendly titanic disaster challenge.
These are the key steps that we will go over:

- Cleaning the data
- Training a machine learning model using decision trees in Sklearn
- Making a submission to Kaggle using the predictions from our training model
Read 28 tweets
17 Apr
This is a complete roadmap for mastering Python.

๐Ÿงต ๐Ÿ‘‡๐Ÿป
Basic Topics

- Variables
- Conditions
- Chained Conditionals
- Operators
- Control Flow (If/Else)
- Loops and Iterables
- Basic Data Structures
- Functions
- Mutable vs Immutable
- Common Methods
- File IO
Intermediate Topics

- Object Oriented Programming
- Data Structures
- Comprehensions
- Lambda Functions
- Map, Filter
- Collections
- *args & **kwargs
- Inheritance
- Dunder Methods
- PIP
- Environments
- Modules
- Async IO
Read 6 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!

:(