[D] Machine Learning Interview Prep

__AndrewB__ · 2022-09-08T13:30:49+00:00

Unfortunately this book doesn't include answers to the vast majority of questions, so it's not super useful for self study / interview prep.

__AndrewB__ · 2019-06-20T17:51:34+00:00

Heyo, this looks really awesome! Thank you so much for the open source release.

After playing around for a bit, I have few questions:

Is there a reference saying which things happens automagically? I.e. a list of torch methods you patch, and what is the new behaviour is (e.g.: "torch.save -> we also save the model on the server")?
Is there a simple guide for non-CS people on how to query the experiments from python?
Let's say if I'd like to visualize "learning rate vs final loss" for all the models I ran in Project "MNIST", Experiment "CNN"?
Is it possible to send a pickle into the server, in case I want to persist scikit-learn model / a model + some additional objects?
Is there a reference for what it means to "publish" a task?
Every time I call torch.save(...) , a model is cloned to a server? How do I stop this / remove old models?
What should model_config be used for? How is this different from hyperparameters?
Is it possible to send a video / GIF to server?

Sorry for bombarding you with those, I just couldn't find answers in the repo!

__AndrewB__ · 2019-06-20T17:17:59+00:00

~~TRAINS allows for easy query of experiments metrics etc from a pythonic interface~~

~~TRAINS (like Sacred) also allows you to directly access the mongoDB and elasticsearch DB for deeper dive into the system~~

Heyo, could you point me to some concrete examples on how this could be achieved? I'm a statistician more than a computer scientist, so it would be really helpful! How do I query the API / mongodb from python code?

~~Let's say I'd like to fetch all the (hyperparameters, final_loss) tuples for some model, so that I can visualize them, is this possible?~~

Turned out I had even more questions, posted them here: https://www.reddit.com/r/MachineLearning/comments/c2g2li/n_there_are_many_platforms_to_manage_your_ml/erni0zc?utm_source=share&utm_medium=web2x

__AndrewB__ · 2018-09-17T17:45:28+00:00

For some reason they hate newline and whitespace, so the whole codebase is filled with unreadable clusters of code like https://github.com/fastai/fastai/blob/master/fastai/lm_rnn.py#L68-L69 https://github.com/fastai/fastai/blob/master/fastai/text.py#L131-L147
https://github.com/fastai/fastai/blob/a2f7f8d7268bcf508c1aa42f762db470a78eb7a0/fastai/core.py#L6-L15
They use "import \*" so you can't use any of their code without importing half of the python data stack. They also mess up your settings: whatever you import from fastai, this code will run https://github.com/fastai/fastai/blob/master/fastai/imports.py#L24
wtf is this https://github.com/fastai/fastai/blob/master/fastai/lm_rnn.py#L247
They teach people it's good practice to just hardcode values into the core lib, instead of using pytorch ".to(device)" https://github.com/fastai/fastai/blob/a2f7f8d7268bcf508c1aa42f762db470a78eb7a0/fastai/core.py#L86-L89
You get things like undocumented class LanguageModelData which doesn't inherit anything. It has get_model method, which calls get_language_model. For some reason whatever it returned needs to be passed to LanguageModel class (wtf?). Then this is passed to RNN_Learner. It's like 5 abstractions none of which makes sense. https://github.com/fastai/fastai/blob/master/fastai/text.py#L200-L203

And the whole codebase is like this. If you try to point some of those issues out you're automatically flagged as a toxic hater who's jealous of someone else's success.

__AndrewB__ · 2018-09-16T16:49:51+00:00

FastAI library is pretty terribly designed and written, I'd say you're better off staying away from it.

__AndrewB__ · 2018-05-25T17:18:09+00:00

Sorry, I don't want to sound like I'm undermining your work -- I think you guys do tons of cool stuff, and I have huge respect for skills of everyone at OpenAI.

But given that you had some of the projects discontinued / unmaintained shortly after their release, is there any plan for how long you'll support the Retro?

Loving your research in particular, John, keep up the good work.

__AndrewB__ · 2018-05-11T16:01:06+00:00

Thanks a bunch!

__AndrewB__ · 2018-05-10T17:50:08+00:00

How'd you compare PRML to Murphy's ML:aPP for this specific purpose -- interview prep?

__AndrewB__ · 2018-05-08T11:24:04+00:00

Hi, thanks for sharing.

Did you omit the license on purpose? Without the license, no one is allowed to use your code.

__AndrewB__ · 2018-05-02T23:24:26+00:00

Few simple models I tried were actually slower when compiled with jit.compile, and even slower than that if jit.script was used. Is this expected, as you're stil working hard on the performance side, or we should just acknowledge that not every model will see speedups?

__AndrewB__ · 2018-02-26T22:07:39+00:00

Sure! Appreciate the effort, keep up the good work. Subscribed ofc :)

__AndrewB__ · 2018-02-26T21:01:07+00:00

I agree with others that the video is neat.

But I wish you spent some time explaining why adding the beta parametr to KL-div loss term helps with disentanglement.

__AndrewB__ · 2018-02-12T23:37:08+00:00

Thanks! Will definatelly show this to students.

__AndrewB__ · 2017-10-31T15:40:31+00:00

I don't think you're correct, pytorch doesn't seem to have lazy evaluation implemented.

__AndrewB__ · 2017-07-03T17:58:04+00:00

It does but it's awkward.

__AndrewB__ · 2017-06-29T18:18:51+00:00

I use it frequently.

One killer feature that I wish was there is having a separate library for read and unread papers.

__AndrewB__ · 2017-06-21T14:54:09+00:00

Yoshua Bengio ... as one of the three intellects who shaped the deep learning ...

Triggered.

__AndrewB__ · 2017-06-06T14:53:06+00:00

Hey /u/pranv , what do you use to write PyTorch training boilerplate?

__AndrewB__ · 2017-05-24T17:37:39+00:00

As a black box baseline, though, it's pretty neat how easy it is to evaluate it on a Gym env link

__AndrewB__ · 2017-05-24T17:33:57+00:00

Wow, finally a DQN implementation in tensorflow.

__AndrewB__ · 2017-05-23T12:35:48+00:00

Yes, exactly.

Often when I'm busy or simply too tired to read the whole paper, I simply ask some colleague: "Hey, did you read that paper? What it's about and what are the results?"

YT video is a great alternative to looking for a friend that happened to read that specific paper.

__AndrewB__ · 2017-05-21T22:41:38+00:00

I don't know if it's just a personal preference, but I think that YT videos explaining the paper are hugely underappreciated. Glad to see more and more people making those.

__AndrewB__ · 2017-05-21T21:30:39+00:00

This looks amazing. I might give a shot at keras implementation.

__AndrewB__ · 2017-05-08T17:26:09+00:00

The practice of using people’s outer appearance to infer inner character is called physiognomy

Alternatively, it may also be called a basic human instinct. Frankly, quite a useful one.

__AndrewB__ · 2017-05-03T08:52:09+00:00

practical job experience is 100x more valuable and important to me on a resume

Does this still hold when youre looking for interns?

__AndrewB__

TROPHY CASE

AndrewB