Before we get started, I'd like to address the elephant in the room. Machine learning is a really big field, and there's a lot to talk about. To keep this session short, we'll only cover the basics of machine learning. We'll learn about many different algorithms and techniques, but you won't be able to go into much depth. That's OK. Learning about the theory behind machine learning is a really big part of the journey, so it's not a problem that we won't cover this. It will give you a good start and allow you to evaluate the methods we use and hear about the challenges of large-scale applications.
The final stage of the machine learning workflow will involve deploying the learned model to production. That's where it gets interesting. In the real world, there are many sources of problems and challenges. We'll discuss a few here, and at the end of the session I'll suggest ways to address the problems and help you build a good machine learning system. Even if you've never worked with machine learning before, you'll have a good idea of where to start.
The other thing that makes machine learning so attractive is that it's a huge field. If you've never worked with machine learning before, you may feel overwhelmed. We're not just talking about a few lines of code, we're talking about big libraries, tools, and frameworks. If you don't already have a good foundation in computer science, you may not have much experience with the theory that many machine learning methods are based upon. That's OK, we'll help you out.
Some applications, such as Java, require that the application directories and libraries are updated to a newer version before the user can update. However, there are some applications that are not explicitly API-specific, such as the X Window System, the X server source code, are automatically updated as required.
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