11–20 of 122 posts
Does anyone have any resources for people with more advanced ML experience?
2. Implement their methods from scratch (i.e. numpy not pytorch)
3. Experiment a bit, tweaking the models/algs to gain intuition
4. Repeat 1-3
Honestly, skip all of the courses. Pick a problem to solve, start googling for common models that are used to solve the problem, then go on github, find code that solves that problem or a similar one. Download the code and start working with it, change it, experiment. All of the theory and such is mostly worthless, its too much to learn from scratch and you will probably use very little of it. There is so much ml cod…
This. Learn top down, not bottom up. Watch maybe one or two short videos on back propagation. You don't need to be muddled in the theory and the math - you can become productive right away. Once you start playing with pytorch and tensorflow models (train them yourself or do transfer learning), you'll start to develop an intuition for how the network graphs fit together. You'll also pick up tools like tensorboard. Als…
I’m someone who took all those math courses and some grad ML coursework. And what that means is that I’m qualified to try and hack together some specific research level things that a practitioner will be confused by, and then try to write a paper about it. It doesn’t mean I’m qualified to do what the practitioner does. Frankly I never ran my code on anything other than MNIST yet and don’t know the different architectures or applications well, since they’re not directly what I work on. They’re just different things, as I see it.
Honestly, skip all of the courses. Pick a problem to solve, start googling for common models that are used to solve the problem, then go on github, find code that solves that problem or a similar one. Download the code and start working with it, change it, experiment. All of the theory and such is mostly worthless, its too much to learn from scratch and you will probably use very little of it. There is so much ml cod…
I hope you're trolling because this is a guaranteed way to climb a peak of stupidity [1]. If OP is determined to get a bit deeper than 30 min guides on Medium, there is sure theory to learn. But it is merely second year of college, and probably you would like to skip Kolmogorov axiomatics and measure theory, it won't hurt your understanding of bleeding edge researches. [1] https://en.m.wikipedia.org/wiki/Dunning%E2%8…
Very few people can read about bias variance trade off and in the course of using a model, understand how to take that concept and directly apply it to the problem they are solving. In retrospect, they can look back and understand the outcomes. Also, most theory is useless in the application of ML, and only usefull in the active research of new machine learning methods and paradigms. Courses make the mistake of mixing in that useless information.
The same thing is true of the million different optimizers for neural networks. Why different ones work better in different cases is something you would learn when trying to squeeze out performance on a neural network. Who here is intelligent enough to read a bunch about SGD and optimization theory (Adam etc), understand the implications, and then use different optimizers in different situations? No one.
I'm much better off having a mediocre NN, googling, "How to improve my VGG image model accuracy", and then finding out that I should tweak learning rates. Then I google learning rate, read a bit, try it on my model. Rinse and repeat.
Does anyone have any resources for people with more advanced ML experience?
Honestly, skip all of the courses. Pick a problem to solve, start googling for common models that are used to solve the problem, then go on github, find code that solves that problem or a similar one. Download the code and start working with it, change it, experiment. All of the theory and such is mostly worthless, its too much to learn from scratch and you will probably use very little of it. There is so much ml cod…
No. You will not get beyond copy-paste level without being comfortable with ML foundations. That doesn't mean you need to be able to prove variational inference bounds in your sleep, but you'll want to know why we need things like lower bounds for approximate inference.
Honestly, skip all of the courses. Pick a problem to solve, start googling for common models that are used to solve the problem, then go on github, find code that solves that problem or a similar one. Download the code and start working with it, change it, experiment. All of the theory and such is mostly worthless, its too much to learn from scratch and you will probably use very little of it. There is so much ml cod…
> All of the theory and such is mostly worthless No. You will not get beyond copy-paste level without being comfortable with ML foundations. That doesn't mean you need to be able to prove variational inference bounds in your sleep, but you'll want to know why we need things like lower bounds for approximate inference.
but everyone else in here is hyping fastai, which is not just copy-paste but wrapped copy-paste at that (so you're not even learning pytorch).
Earlier quoted context omitted.
I hope you're trolling because this is a guaranteed way to climb a peak of stupidity [1]. If OP is determined to get a bit deeper than 30 min guides on Medium, there is sure theory to learn. But it is merely second year of college, and probably you would like to skip Kolmogorov axiomatics and measure theory, it won't hurt your understanding of bleeding edge researches. [1] https://en.m.wikipedia.org/wiki/Dunning%E2%8…
My opinion is that the theory starts to make sense after you know how to use the models and have seen different models produce different results. Very few people can read about bias variance trade off and in the course of using a model, understand how to take that concept and directly apply it to the problem they are solving. In retrospect, they can look back and understand the outcomes. Also, most theory is useless…
Does anyone have any resources for people with more advanced ML experience?
1. Find a paper you like/admire 2. Implement their methods from scratch (i.e. numpy not pytorch) 3. Experiment a bit, tweaking the models/algs to gain intuition 4. Repeat 1-3
lol this is basically impossible and completely pointless. please show me a numpy implementation of BERT or CycleGAN or deformable convolutions (note that jax != numpy). it's like suggesting implementing a kernel to someone who wants to learn about virtual memory or scheduling.
better advice would be take a paper and implement the model using pytorch without looking at their implementation and fiddle with that.
Earlier quoted context omitted.
> All of the theory and such is mostly worthless No. You will not get beyond copy-paste level without being comfortable with ML foundations. That doesn't mean you need to be able to prove variational inference bounds in your sleep, but you'll want to know why we need things like lower bounds for approximate inference.
>No. You will not get beyond copy-paste level without being comfortable with ML foundations. but everyone else in here is hyping fastai, which is not just copy-paste but wrapped copy-paste at that (so you're not even learning pytorch).
How to learn ML? Do fastai + reading Daphne Koller's and Chris Bishop's books on PGMs + re-implementing a paper on Gaussian process classification + another paper on GNNs + ....
Earlier quoted context omitted.
My opinion is that the theory starts to make sense after you know how to use the models and have seen different models produce different results. Very few people can read about bias variance trade off and in the course of using a model, understand how to take that concept and directly apply it to the problem they are solving. In retrospect, they can look back and understand the outcomes. Also, most theory is useless…
I personally started with Kaggle competitions and lots of googling (duckduckgoing right?), but quite quickly hit the wall of not understanding, I felt like a mindless creature who makes a decision based on couple of guides out there. Watching lectures from Andrew Ng, reading some books helped a lot, but I can't see a reason why one doesn't wanna start with theory. It's no gold and glitter, and no one promised you tha…
Then I tried Kaggle's mini-course. It kickstarted me into ML and motivated me to learn the theory as I go. For example, when I got to apply Random Forest Regressor, I went to Wikipedia and tried to read on it. Got some idea. And the progress is good.
Maybe for some of us, I think top-down is motivating and makes the learning process enjoyable.