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When learning, I like to continually create a mental model for what will happen, and check if I'm correct. It's like doing problem sets in math, then plugging i

When learning, I like to continually create a mental model for what will happen, and check if I'm correct. It's like doing problem sets in math, then plugging in the problem to MathLab to see the result. I've never used this Microsoft product, but if lets you take educated guesses at what will work, and gives you some insights into the intermediate steps, then its useful as a check that your mental model of machine learning is becoming more coherent and useful. Plus, if you slot in something and it gives a better output, you can go back to your studies with a new target of finding out why X param changed things.

shostack 10y ago
The particular concern that sparked this with me is the concern of over-fitting to the data set. I don't know enough about ML to know how much of a risk that might be, but with a tool like this I wonder if that becomes obvious, or if you risk taking away false learnings just because you saw the output you hoped for, despite it being perhaps horribly overfit. Again, that's just one example, and the instant visual feedback is awesome (I'm a visual learner, so that's huge). But at the end of the day, I know that there is a lot of math and code under the pretty graphics, and at some point I'll need to tackle that to make sure I am actually learning this and not just making assumptions based on what I can eyeball with some visualizations.
- Joof 10y ago
  Learn to multiply matrices (you can probably Google this). Note that AB != BA in matrix math. Learn derivatives and how to do them with a lookup table. Learn what log() means (the inverse of some number to a power). That's enough to implement and understand neural networks. You'll fumble around a lot more than you have to, but you can figure it out. Honestly, you could probably fight your way through Ng's class with just matrix multiplication, which you can learn in less than an hour fairly easily.

shostack 10y ago

The particular concern that sparked this with me is the concern of over-fitting to the data set. I don't know enough about ML to know how much of a risk that might be, but with a tool like this I wonder if that becomes obvious, or if you risk taking away false learnings just because you saw the output you hoped for, despite it being perhaps horribly overfit. Again, that's just one example, and the instant visual feedback is awesome (I'm a visual learner, so that's huge). But at the end of the day, I know that there is a lot of math and code under the pretty graphics, and at some point I'll need to tackle that to make sure I am actually learning this and not just making assumptions based on what I can eyeball with some visualizations.

Joof 10y ago
Learn to multiply matrices (you can probably Google this). Note that AB != BA in matrix math. Learn derivatives and how to do them with a lookup table. Learn what log() means (the inverse of some number to a power). That's enough to implement and understand neural networks. You'll fumble around a lot more than you have to, but you can figure it out. Honestly, you could probably fight your way through Ng's class with just matrix multiplication, which you can learn in less than an hour fairly easily.