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Data similarity has such a simple visual interpretation that it will light all the bulbs in your head.

The mathematical magic tells you that similarity is given by the inner product. Have you thought about why?

This is how elementary geometry explains it all.

β A thread. β

The mathematical magic tells you that similarity is given by the inner product. Have you thought about why?

This is how elementary geometry explains it all.

β A thread. β

The early access of my Mathematics of Machine Learning book is launching today!

One chapter per week, we go from basics to the internals of neural networks. We are starting with vector spaces, the scene where machine learning happens.

Here is why they are so important!

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One chapter per week, we go from basics to the internals of neural networks. We are starting with vector spaces, the scene where machine learning happens.

Here is why they are so important!

π§΅ ππ½

The Mathematics of Machine Learning book early release is launching in September 1st! Exciting times are ahead :)

If you are interested in understanding the mathematics of machine learning, this is the book for you.

tivadar.gumroad.com/l/mathematics-β¦

If you are interested in understanding the mathematics of machine learning, this is the book for you.

tivadar.gumroad.com/l/mathematics-β¦

In the early access program, I'll release the sections of this book as I write them.

During our time together, my goal is to guide you through the inner workings of machine learning, from high school mathematics to backpropagation.

During our time together, my goal is to guide you through the inner workings of machine learning, from high school mathematics to backpropagation.

This is the release calendar for 2021.

Part 1: Linear algebra

1. Vector spaces (September 1st)

2. Normed spaces (September 8th)

3. Inner product spaces (September 15th)

4. Linear transformations (September 22th)

Part 1: Linear algebra

1. Vector spaces (September 1st)

2. Normed spaces (September 8th)

3. Inner product spaces (September 15th)

4. Linear transformations (September 22th)

Machine learning is more than function fitting.

Even though most of us are introduced to the subject through this example, fitting functions to a training dataset seemingly doesn't give us any deep insight about the data.

This is what's working behind the scenes!

π§΅ ππ½

Even though most of us are introduced to the subject through this example, fitting functions to a training dataset seemingly doesn't give us any deep insight about the data.

This is what's working behind the scenes!

π§΅ ππ½

How to build a good understanding of math for machine learning?

I get this question a lot, so I decided to make a complete roadmap for you. In essence, three fields make this up: calculus, linear algebra, and probability theory.

Let's take a quick look at them!

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I get this question a lot, so I decided to make a complete roadmap for you. In essence, three fields make this up: calculus, linear algebra, and probability theory.

Let's take a quick look at them!

π§΅ π

1. Linear algebra.

In machine learning, data is represented by vectors. Essentially, training a learning algorithm is finding more descriptive representations of data through a series of transformations.

Linear algebra is the study of vector spaces and their transformations.

In machine learning, data is represented by vectors. Essentially, training a learning algorithm is finding more descriptive representations of data through a series of transformations.

Linear algebra is the study of vector spaces and their transformations.

Simply speaking, a neural network is just a function mapping the data to a high-level representation.

Linear transformations are the fundamental building blocks of these. Developing a good understanding of them will go a long way, as they are everywhere in machine learning.

Linear transformations are the fundamental building blocks of these. Developing a good understanding of them will go a long way, as they are everywhere in machine learning.

How you play determines who you are.

You might be surprised, but I gained a lot from playing games. Board games, video games, all of them. Playing is a free-time activity, but it can teach a lot about life and work.

This thread is about the most important lessons I learned.

You might be surprised, but I gained a lot from playing games. Board games, video games, all of them. Playing is a free-time activity, but it can teach a lot about life and work.

This thread is about the most important lessons I learned.

1. Taking responsibility for your mistakes.

Mistakes are the best way to learn, but you can do so by taking responsibility instead of looking for excuses. Stop blaming bad luck, lag, teammates, or anything else.

Be your own critic and identify where you can improve.

2/8

Mistakes are the best way to learn, but you can do so by taking responsibility instead of looking for excuses. Stop blaming bad luck, lag, teammates, or anything else.

Be your own critic and identify where you can improve.

2/8

2. Actively focus on improvement.

Contrary to popular belief, "just doing it" is not an effective way to learn. Identifying flaws in your game, setting progressive goals, and keeping yourself accountable relentlessly supercharges the process. Play (work) with purpose.

Contrary to popular belief, "just doing it" is not an effective way to learn. Identifying flaws in your game, setting progressive goals, and keeping yourself accountable relentlessly supercharges the process. Play (work) with purpose.