Mistakes should be celebrated.

I used to struggle with everything I started to do until I became skilled in it.

The key was to recognizing what I did wrong and going back to fix it. Over and over and over again.

Here is my list of failures that led me to success!

🧵 👇🏽
I was a bad student in school. The most difficult subject for me was mathematics, which I almost failed at one time.

Once I developed an interest, I started to improve very slowly.

Years later, I obtained a PhD in it after solving a problem that has been unsolved for decades.
As a teenager, I was overweight and physically weak. All fat, no muscle.

I was unable to do a single pushup.

Years later, I regularly do 25-50 pushups with one arm only. (Learning to do just a single one-armed pushup took me five years.)
My first blog post was horrible and practically had a negative view count.

It was bland, uninteresting, and difficult to follow.

So, I focused to write better. The quality of my posts started to improve.

Years later, I published a post that became No. 1 trending on Medium.
I started coding at 26. (I am 30 now.)

At first, writing a simple Python script without messing up the tabulation was challenging.

Years later, that small Python package I wrote is the most used active learning library.
Here is what I know. I am not smart or talented. My body and mind are average.

Every success of mine started as a failure.

I am just pretty fucking persistent and can't be discouraged.

Success is a result of putting in the work consistently, every day.
TL;DR: go and take a small step towards being the person you want to be a decade from now.

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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
2 Mar
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.
Read 16 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

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