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Mathematics of Machine Learning
My journey in machine learning has been full of twists and turns. By training, I am a mathematician. During my PhD, I was doing research in approximation theory, a field close to the heart of learning algorithms.
I saw the stunning applications of machine learning in the life sciences, I was hooked. Since then, I am passionate about neural networks and their inner workings. I love taking them apart piece by piece, understanding every cog in the machine, then putting it all back together.
I believe that to really understand something, you have to build it by yourself from scratch. This is why I am writing a book about the mathematics of machine learning, guiding you from introductory linear algebra to implementing a neural network from scratch.
So, you want to master machine learning. Even though you have experience in the field, sometimes you still feel that something is missing. A look behind the curtain.
Have you ever felt the learning curve to be so sharp that it was too difficult even to start? The theory was so dry and seemingly irrelevant that you were unable to go beyond the basics?
If so, I am building something for you. I am working to create the best resource to study the mathematics of machine learning out there.
I saw the stunning applications of machine learning in the life sciences, I was hooked. Since then, I am passionate about neural networks and their inner workings. I love taking them apart piece by piece, understanding every cog in the machine, then putting it all back together.
I believe that to really understand something, you have to build it by yourself from scratch. This is why I am writing a book about the mathematics of machine learning, guiding you from introductory linear algebra to implementing a neural network from scratch.
So, you want to master machine learning. Even though you have experience in the field, sometimes you still feel that something is missing. A look behind the curtain.
Have you ever felt the learning curve to be so sharp that it was too difficult even to start? The theory was so dry and seemingly irrelevant that you were unable to go beyond the basics?
If so, I am building something for you. I am working to create the best resource to study the mathematics of machine learning out there.
- GenresMathematicsTechnical
Unknown Binding
Published January 11, 2023
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Displaying 1 of 1 review
September 21, 2022
Danka writes extensive Bottom-up approach to Machine Learning
In addition to his approach, he explains through practical application.
I strongly believe, bottom-up approach is how one understands in-depth.
So what do I do to understand?
1. Read formal definition
2. Try to summarize in your words
3. Ask questions, Why, How, Where
4. Figure out problems, where you'd require it
5. Take breaks, repeat
One thing, that went to my mind as I was going through, Where do I apply this?
Take Compact Sets, we use it in Optimization - where exactly?
Why care about bottom-up approach?
Imagine, formulating algorithms which could mitigate heart-attacks by interventions using advance predictions.
Deus Vult
Gottfried
In addition to his approach, he explains through practical application.
I strongly believe, bottom-up approach is how one understands in-depth.
So what do I do to understand?
1. Read formal definition
2. Try to summarize in your words
3. Ask questions, Why, How, Where
4. Figure out problems, where you'd require it
5. Take breaks, repeat
One thing, that went to my mind as I was going through, Where do I apply this?
Take Compact Sets, we use it in Optimization - where exactly?
Why care about bottom-up approach?
Imagine, formulating algorithms which could mitigate heart-attacks by interventions using advance predictions.
Deus Vult
Gottfried
Displaying 1 of 1 review