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You probably know Santiago from his Twitter. On Twitter, every day, he shares a lot of practical aspects of artificial intelligence. Many thanks, Santiago, for joining us today. Welcome. (2:39) Santiago: Thanks for welcoming me. (3:16) Alexey: Prior to we go into our primary subject of moving from software program engineering to artificial intelligence, possibly we can start with your history.
I started as a software program developer. I mosted likely to university, obtained a computer science level, and I started constructing software program. I think it was 2015 when I decided to opt for a Master's in computer scientific research. At that time, I had no idea concerning artificial intelligence. I really did not have any kind of passion in it.
I know you have actually been making use of the term "transitioning from software application engineering to artificial intelligence". I like the term "adding to my ability set the machine learning abilities" more due to the fact that I believe if you're a software application designer, you are currently giving a great deal of value. By including equipment discovering currently, you're augmenting the effect that you can have on the industry.
Alexey: This comes back to one of your tweets or perhaps it was from your course when you contrast 2 techniques to learning. In this instance, it was some problem from Kaggle about this Titanic dataset, and you just discover just how to resolve this trouble making use of a details tool, like decision trees from SciKit Learn.
You initially find out math, or linear algebra, calculus. When you understand the mathematics, you go to device knowing theory and you learn the theory. After that four years later on, you ultimately come to applications, "Okay, exactly how do I use all these four years of math to address this Titanic issue?" Right? So in the former, you kind of save on your own a long time, I believe.
If I have an electric outlet here that I need replacing, I don't intend to go to college, spend 4 years recognizing the mathematics behind electrical power and the physics and all of that, simply to transform an outlet. I would certainly instead begin with the electrical outlet and find a YouTube video clip that helps me undergo the problem.
Negative analogy. You get the idea? (27:22) Santiago: I really like the concept of beginning with an issue, trying to toss out what I know up to that issue and recognize why it doesn't work. Order the tools that I need to solve that problem and begin excavating much deeper and deeper and deeper from that point on.
That's what I generally advise. Alexey: Perhaps we can talk a bit concerning discovering resources. You discussed in Kaggle there is an introduction tutorial, where you can get and find out exactly how to choose trees. At the start, before we started this meeting, you stated a couple of publications.
The only need for that training course is that you recognize a little bit of Python. If you go to my account, the tweet that's going to be on the top, the one that claims "pinned tweet".
Even if you're not a designer, you can start with Python and function your way to more artificial intelligence. This roadmap is concentrated on Coursera, which is a system that I really, really like. You can audit every one of the courses free of cost or you can spend for the Coursera membership to obtain certifications if you desire to.
To make sure that's what I would certainly do. Alexey: This returns to one of your tweets or possibly it was from your program when you contrast two techniques to learning. One strategy is the trouble based method, which you just discussed. You find an issue. In this situation, it was some problem from Kaggle about this Titanic dataset, and you just discover just how to address this problem using a certain tool, like choice trees from SciKit Learn.
You first learn mathematics, or straight algebra, calculus. When you understand the math, you go to machine understanding theory and you learn the concept.
If I have an electrical outlet right here that I need changing, I don't desire to go to college, spend 4 years understanding the mathematics behind electrical power and the physics and all of that, simply to alter an outlet. I prefer to begin with the electrical outlet and locate a YouTube video that aids me undergo the problem.
Santiago: I actually like the concept of beginning with a problem, trying to toss out what I recognize up to that issue and understand why it doesn't work. Grab the devices that I need to solve that trouble and start digging deeper and deeper and deeper from that point on.
Alexey: Possibly we can chat a bit regarding discovering resources. You stated in Kaggle there is an introduction tutorial, where you can obtain and discover how to make choice trees.
The only demand for that training course is that you understand a bit of Python. If you're a programmer, that's a fantastic starting point. (38:48) Santiago: If you're not a programmer, after that I do have a pin on my Twitter account. If you go to my profile, the tweet that's mosting likely to get on the top, the one that claims "pinned tweet".
Even if you're not a developer, you can start with Python and work your way to even more artificial intelligence. This roadmap is concentrated on Coursera, which is a system that I really, really like. You can examine every one of the courses free of cost or you can spend for the Coursera membership to obtain certifications if you wish to.
That's what I would do. Alexey: This returns to among your tweets or maybe it was from your course when you compare two techniques to learning. One strategy is the issue based method, which you simply talked about. You locate a trouble. In this situation, it was some issue from Kaggle regarding this Titanic dataset, and you simply find out exactly how to fix this problem making use of a specific device, like decision trees from SciKit Learn.
You first find out mathematics, or direct algebra, calculus. When you understand the math, you go to machine discovering theory and you learn the theory.
If I have an electric outlet right here that I require replacing, I do not wish to most likely to university, spend four years understanding the math behind power and the physics and all of that, just to change an outlet. I would certainly instead begin with the electrical outlet and discover a YouTube video clip that aids me go with the issue.
Negative example. However you get the concept, right? (27:22) Santiago: I actually like the idea of beginning with a problem, attempting to throw out what I understand as much as that issue and understand why it does not work. Grab the devices that I need to fix that problem and start excavating much deeper and deeper and deeper from that point on.
To make sure that's what I normally advise. Alexey: Possibly we can speak a bit about learning sources. You stated in Kaggle there is an intro tutorial, where you can obtain and find out exactly how to make choice trees. At the start, before we began this meeting, you stated a number of books as well.
The only demand for that program is that you recognize a little bit of Python. If you're a programmer, that's an excellent base. (38:48) Santiago: If you're not a programmer, then I do have a pin on my Twitter account. If you go to my account, the tweet that's mosting likely to be on the top, the one that claims "pinned tweet".
Also if you're not a developer, you can begin with Python and work your method to more artificial intelligence. This roadmap is focused on Coursera, which is a system that I actually, truly like. You can audit all of the training courses free of cost or you can pay for the Coursera subscription to obtain certifications if you intend to.
To make sure that's what I would do. Alexey: This returns to among your tweets or possibly it was from your training course when you compare 2 methods to learning. One technique is the trouble based technique, which you simply discussed. You locate a problem. In this case, it was some problem from Kaggle about this Titanic dataset, and you just find out just how to solve this trouble making use of a details device, like decision trees from SciKit Learn.
You initially learn mathematics, or straight algebra, calculus. When you understand the mathematics, you go to equipment learning theory and you discover the concept.
If I have an electrical outlet below that I need replacing, I don't intend to most likely to university, spend 4 years comprehending the math behind electricity and the physics and all of that, just to alter an electrical outlet. I would certainly instead begin with the electrical outlet and discover a YouTube video that assists me experience the trouble.
Santiago: I truly like the idea of starting with a problem, attempting to throw out what I understand up to that trouble and understand why it doesn't function. Order the devices that I require to fix that issue and begin digging much deeper and deeper and deeper from that point on.
Alexey: Perhaps we can talk a little bit about learning resources. You stated in Kaggle there is an intro tutorial, where you can get and find out exactly how to make choice trees.
The only need for that course is that you know a little bit of Python. If you go to my account, the tweet that's going to be on the top, the one that claims "pinned tweet".
Even if you're not a designer, you can begin with Python and work your method to more device knowing. This roadmap is concentrated on Coursera, which is a platform that I actually, truly like. You can audit all of the programs free of cost or you can spend for the Coursera registration to obtain certificates if you want to.
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