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>> But if you want to truly understand how these models work, you need to make effort to study the basics. I was very confused by this in the beginning of my j

>> But if you want to truly understand how these models work, you need to make effort to study the basics. I was very confused by this in the beginning of my journey. I was trying to learn everything involved with ML/DL, but in the end everything is already implemented with APIs, and your boss doesnt care if you know how to implement a MLP from scratch or if you use Tensorflow. My (poor) analogy is: you don't need to know how a car works (or how to build one) in every detail to drive it. When I understood it, it was liberating.

mudrockbestgirl 4y ago
I agree, and I think that's the first use case I mentioned above. You can use Deep Learning libraries and pre-made models without a deep understanding of anything and you get some nice results. And that's great. What's not so great is the huge number of people believing to understand something when they don't, i.e. the illusion of knowledge they're getting from some of these marketing-driven courses and MOOCs. I see that in job applications. Every resume has "Deep Learning, PyTorch, Tensorflow" on it now, but if you ask them why something works (or why a variation may not work) these candidates have no idea. And for some jobs that's totally fine, but for other jobs it's not. And the problem is when you can't tell the difference. It's kind of like putting "compilers" on your resume because you've managed to run gcc.
- neodypsis 4y ago
  > but if you ask them why something works (or why a variation may not work) Interesting. Do you have an example? Is it common for people to practice ML problem-solving à la LeetCode nowadays?
  - mudrockbestgirl 4y ago
    It's the opposite of leetcode because it tests understanding, not memorization. For example, you could ask why position embeddings are necessary and what would happen without, reasoning behind certain terms in an objective function and what would happen without them, which part of an architecture the bottleneck for convergence is, intuitively what tuning a certain hyperparameter does, show them a timeseries of gradients for something that doesn't convergence and ask what's wrong with them, etc. I'm just making these up because the questions we previously asked were domain-specific to our applications, e.g. "why is this specific learning objective hard" or "what would you modify to help generalization in case X" These questions are very easy to talk about for someone with a strong ML background. They may not always know the answer and often there is no right answer, but they can make reasonable guesses and have a thought process around it. Someone who just took a MOOC likely has no idea how to even approach the question.

mudrockbestgirl 4y ago

I agree, and I think that's the first use case I mentioned above. You can use Deep Learning libraries and pre-made models without a deep understanding of anything and you get some nice results. And that's great. What's not so great is the huge number of people believing to understand something when they don't, i.e. the illusion of knowledge they're getting from some of these marketing-driven courses and MOOCs. I see that in job applications. Every resume has "Deep Learning, PyTorch, Tensorflow" on it now, but if you ask them why something works (or why a variation may not work) these candidates have no idea. And for some jobs that's totally fine, but for other jobs it's not. And the problem is when you can't tell the difference. It's kind of like putting "compilers" on your resume because you've managed to run gcc.

neodypsis 4y ago
> but if you ask them why something works (or why a variation may not work) Interesting. Do you have an example? Is it common for people to practice ML problem-solving à la LeetCode nowadays?
- mudrockbestgirl 4y ago
  It's the opposite of leetcode because it tests understanding, not memorization. For example, you could ask why position embeddings are necessary and what would happen without, reasoning behind certain terms in an objective function and what would happen without them, which part of an architecture the bottleneck for convergence is, intuitively what tuning a certain hyperparameter does, show them a timeseries of gradients for something that doesn't convergence and ask what's wrong with them, etc. I'm just making these up because the questions we previously asked were domain-specific to our applications, e.g. "why is this specific learning objective hard" or "what would you modify to help generalization in case X" These questions are very easy to talk about for someone with a strong ML background. They may not always know the answer and often there is no right answer, but they can make reasonable guesses and have a thought process around it. Someone who just took a MOOC likely has no idea how to even approach the question.