Besides Kaggle, there are several other competition platforms.

You can use these to

• learn,
• test your skills,
• collaborate with awesome people,
• enhance your resume,
• and possibly earn money.

Take a look at these below, you'll definitely find them useful!

🧵 👇🏽
1. Numerai (numer.ai)

This is quite a special one, since it only contains a single competition.

However, its aims are big: Numerai wants to build the world's first open hedge fund
2. AIcrowd (aicrowd.com)

You can find all sort of competitions here on a wide spectrum, from applied problems to research.
3. DrivenData (drivendata.org)

DrivenData has a special focus on problems with significant potential impact on society, like public health, sustainability, and similar ones.

If you are interested in making the world a better place, this platform is highly recommended!
4. CodaLab (competitions.codalab.org)

CodaLab is an open-source platform, where you can not only compete, but organize competitions using their framework.
5. Zindi (zindi.africa)

Zindi is the leading data science competition platform in Africa, connecting the rich African data science community to some of the most exciting problems in machine learning.
6. Topcoder (topcoder.com/community/learn)

If you are into competitive programming besides data science, Topcoder is for you, as they offer a wide variety of challenges.
7. HackerEarth (hackerearth.com)

Similar to Topcoder, HackerEarth also offers a variety of problems or even mock interview challenges.
8. CrowdANALYTIX (crowdanalytix.com/community)

CrowdANALYTIX is another machine learning competition platform, with the challenge categories of Modelling, Visualization, and Reserarch.
9. BitGrit (bitgrit.net)

They are a young Japanese platform, where you can find relevant job postings as well, in addition to challenges. At the moment, all jobs are located in Japan, but this can change in the future. So, worth keeping in mind!
10. DataHack by Analytics Vidhya (datahack.analyticsvidhya.com)

This is the competition platform by Analytics Vidhya, geared towards creating your portfolio and finding job opportunities in machine learning.
11. Datasource.ai (datasource.ai)

Datasource.ai is aimed towards smaller companies in need of machine learning solutions, who can not necessarily afford them due to its high cost.
12. Innocentive (innocentive.com)

Innocentive is another challenge portal that is specifically aimed towards real-life applications, not only for research and theoretical problems.
13. telesto.ai (telesto.ai)

I left this one for last because I am one of the co-founders :)

We are very new and still in early release. Our aim is to build a platform where you can not only participate, but easily host competitions as well.
We felt that organizing a competition is still difficult, with many manual and work-intensive steps in the middle.

So, we are building the ultimate competition wizard!

If you would like to participate in the beta as competition host, shoot me or @telestoAI a message!
So, now you can see that there is a lot beyond Kaggle.

Have you ever tried some of these platforms? What was your experience?

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More from @TivadarDanka

3 Mar
I am going to tell you the best-kept secret of linear algebra: matrices are graphs and graphs are matrices.

Encoding matrices as graphs is a cheat code, making complex behavior extremely simple to study.

Let me show you how!

🧵 👇🏽
If you looked at the example above, you probably figured out the rule.

Each row is a node, and each element of a row represents a directed edge.

The element in the 𝑖-th row, 𝑗-th column corresponds to the edge in the graph, going from 𝑖 to 𝑗.

(Formal definition below.)
Why is the directed graph representation beneficial for us?

The first example is that the powers of the matrix correspond to walks in the graph.

Take a look at how to calculate the elements of the square of a matrix.
Read 9 tweets
1 Mar
You ask me so often for free online resources about deep learning that I decided to collect my favorite courses!

These topics interest you the most:

🟩 practical deep learning,
🟩 deep learning theory,
🟩 math resources to understand the two above.

Let's see them!

🧵 👇🏽
1️⃣ Practical deep learning.

If you want to take a deep dive straight into the field and want to start training your models right away, hands down the best course for you out there is Practical Deep Learning for Coders by fast.ai. (course.fast.ai)
To move beyond training models and learn about tooling and infrastructure, IMO the best course for you is the Full Stack Deep Learning course by @full_stack_dl.

fall2019.fullstackdeeplearning.com
Read 13 tweets
26 Feb
Have you ever thought about why neural networks are so powerful?

Why is it that no matter the task, you can find an architecture that knocks the problem out of the park?

One answer is that they can approximate any function with arbitrary precision!

Let's see how!

🧵 👇🏽
From a mathematical viewpoint, machine learning is function approximation.

If you are given data points 𝑥 with observations 𝑦, learning essentially means finding a function 𝑓 such that 𝑓(𝑥) approximates the given 𝑦-s as accurately as possible.
Approximation is a very natural idea in mathematics.

Let's see a simple example!

You probably know the exponential function well. Do you also know how to calculate it?

The definition itself doesn't really help you. Calculating the powers where 𝑥 is not an integer is tough.
Read 16 tweets
24 Feb
Conditional probability is one of the central concepts of statistics and probability theory.

Without a way to account for including prior information in our models, statistical models would be practically useless.

Let's see what conditional probability simply means! Image
If 𝐴 are 𝐵 are two events, they are not necessarily independent of each other.

This means that the occurrence of one can give information about the other.

When performing statistical modeling, this is frequently the case.

To illustrate, we will take a look at spam filters!
Suppose that you have 100 mails in your inbox.

40 is spam, 60 is not.

Based only on this information, if you receive a random letter, there is a 40% chance that it is spam.

This is not sufficient to build a decent model for spam detection. Image
Read 7 tweets
23 Feb
What makes it possible to train neural networks with gradient descent?

The fact that the loss function of a network is a differentiable function!

Differentiation can be hard to understand. However, it is an intuitive concept from physics.

💡 Let's see what it really is! 💡
Differentiation essentially describes a function's rate of change.

Let's see how!

Suppose that we have a tiny object moving along a straight line back and forth.

Its movement is fully described by its distance from the starting point, plotted against the time.
What is its average speed in its 10 seconds of travel time?

The average speed is simply defined as the ratio of distance and time.

However, it doesn't really describe the entire movement. As you can see, the speed is sometimes negative, sometimes positive.
Read 11 tweets
22 Feb
You can explain the Bayes formula in pure English.

Even without using any mathematical terminology.

Despite being overloaded with seemingly complex concepts, it conveys an important lesson about how observations change our beliefs about the world.

Let's take it apart!
Essentially, the Bayes formula describes how to update our models, given new information.

To understand why, we will look at a simple example with a twist: coin tossing with an unfair coin.
Let's suppose that we have a magical coin! It can come up with heads or tails when tossed, but not necessarily with equal probability.

The catch is, we don't know the exact probability. So, we have to perform some experiments and statistical estimation to find that out.
Read 14 tweets

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