Is convex optimization important for machine learning

  • How important is optimization in machine learning?

    The process of optimisation aims to lower the risk of errors or loss from these predictions, and improve the accuracy of the model.
    Machine learning models are often trained on local or offline datasets which are usually static.
    Optimisation improves the accuracy of predictions and classifications, and minimises error..

  • Is optimization needed for machine learning?

    The process of optimisation aims to lower the risk of errors or loss from these predictions, and improve the accuracy of the model.
    Machine learning models are often trained on local or offline datasets which are usually static.
    Optimisation improves the accuracy of predictions and classifications, and minimises error..

  • Why is optimization important in machine learning?

    The process of optimisation aims to lower the risk of errors or loss from these predictions, and improve the accuracy of the model.
    Machine learning models are often trained on local or offline datasets which are usually static.
    Optimisation improves the accuracy of predictions and classifications, and minimises error..

Convex optimization has become an essential tool in machine learning because many real-world problems can be modeled as convex optimization problems. For example, in classification problems, the goal is to find the best hyperplane that separates the data points into different classes.

Why are we interested in convex functions?

Because the optimization process / finding the better solution over time, is the learning process for a computer

I want to talk more about why we are interested in convex functions

The reason is simple: convex optimizations are "easier to solve", and we have a lot of reliably algorithm to solve

But is the world convex? No

Machine learning algorithms use optimization all the time. We minimize loss, or error, or maximize some kind of score functions. Gradient descent...68

As hxd1011 said, convex problems are easier to solve, both theoretically and (typically) in practice. So, even for non-convex problems, many optimi...3

The most important takeaway is that machine learning is applied to problems where there is no optimal solution available. The best you can do is fi...2

If your interests lie in (convex) optimisation applied to deep learning (you mention transfer learning, which is widely used in practice with neura...1

As I heard from Jerome H. Friedman methods developed in Machine Learning is in fact not belong to Machine Learning community by itself. From my poi...1


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