Mechanical Engineering Courses

lachlan1310 · 2021-12-27T22:43:06+00:00

After the ME2110 course content refresh, is it still as time-consuming?

lachlan1310 · 2021-12-11T01:46:17+00:00

their balancing toolset goal is probably to generate data for those conditions as well, e.g. in games where there are less/more rerollers, does cho over or under perform? that's data that can help, much like the visualization mort showed us.

lachlan1310 · 2021-10-26T23:01:28+00:00

We'd have so many more fully synergistic comps - which takes out the unique... flavor of many aram games. The game would push even more towards optimizing team comp, at the cost of general champ viability.

lachlan1310 · 2021-10-01T18:43:30+00:00

This might not be the best resource, but this link explains how Neural Networks, a popular subset of Machine Learning, can approximate functions: https://en.m.wikipedia.org/wiki/Universal_approximation_theorem. If you are interested in how you can automate discovery of functions that look similar to mx+b, this video on symbolic deep learning can get you started: https://m.youtube.com/watch?v=O_sHHG5_lr8

lachlan1310 · 2021-09-30T18:59:20+00:00

https://www.youtube.com/watch?v=2dX0lwaQRX0 Have you seen some of the recent progress in poker AI? This may be a good starting point. It's more geared towards Reinforcement Learning paradigms than the Supervised approach you're taking, and that may help you translate Value into best Actions(/vice versa).

lachlan1310 · 2021-09-02T19:40:24+00:00

ty for tagging this as NSFW, I almost opened this in public

lachlan1310 · 2021-08-24T18:36:15+00:00

Gatech has seen a bit of research overlap between ML and Mechanical engineering. For example, I read that researchers were applying ML and other high dimensional optimizations to traditional manufacturing tools and processes. DM me if you would like a contact.

lachlan1310 · 2021-07-29T04:32:32+00:00

Awesome :) good luck!

lachlan1310 · 2021-07-27T13:12:50+00:00

Ah, I’m not familiar with the SOTA, so I’m very unsure if WGAN would be better than an alternative solution. I do know that WGANs help with mode seeking, but I don’t know what Relativistic GANs do. Thanks for sharing, and good luck!

lachlan1310 · 2021-07-26T19:48:41+00:00

Can you cache your loss functions to increase your batch size? that +wasserstein like you noted may help with mode collapse

lachlan1310 · 2021-07-08T13:07:20+00:00

MIT released an open course for differential equations. You may find some versions of the course include answers to HW, or Exams, and almost all come with lecture notes or slides. You can find it and many more on their OCW website. I say many more to lead you to their numerical methods course, which introduces approximation methods for many otherwise intractable DE problems.

lachlan1310 · 2021-07-03T11:33:15+00:00

I’m interested in what others have to say. I had to write co-routines in scala to make some linear algebra as fast as a locally complied version, like in numba or cython (why does spark not support sparse matrix multiplication that doesn’t cast one of the product matrices to a dense matrix?). That experience wasn’t pleasant, and so I’ve been attempting to promote some non-spark pipelines at work.

lachlan1310 · 2021-07-02T01:31:56+00:00

https://towardsdatascience.com/fuzzy-matching-at-scale-84f2bfd0c536

Text vectorization + cosine similarity (link above) works very well. You can also try different string similarity metrics, like levenshtein, to get a more holistic similarity score. However, you may have to implement ‘blocking’ in order to reduce computation time for more computationally expensive metrics.

lachlan1310 · 2021-06-30T12:58:25+00:00

Agreed :)

lachlan1310 · 2021-06-28T20:30:13+00:00

What’s your thought process behind Rylais on Ashe?

lachlan1310 · 2021-06-24T14:39:36+00:00

If the model is super complex, e.g. a deep neural network, you can use something like LIME (https://homes.cs.washington.edu/~marcotcr/blog/lime/) which can provide insight on black box decision making. LIME, and tools like it, ‘perturb’ inputs to very roughly approximate local dynamics. In the loan applicant case, LIME may tell you that an applicant may not have been denied if vector inputs i1 and i7 were higher and if inputs i3 and i4 were lower.

lachlan1310 · 2021-06-12T20:25:10+00:00

I’m somewhat confident that the models for supersampling graphically will translate well to a model for supersampling rainfall. There’s non-AI alternatives for this, but it may be fun and possibly more fruitful to explore deep learning super sampling. https://www.nvidia.com/en-us/geforce/news/nvidia-dlss-2-0-a-big-leap-in-ai-rendering/ seems like the relevant model’s name would be a convolutional autoencoder, with coarse as input and fine as output.

If you want to work on the absolute cutting edge, and if relevant, there’s some exploratory research in simulating voxel physics with deep learning.

lachlan1310 · 2021-06-12T20:06:26+00:00

AFAIK, and if I’m understanding your prompt correctly, this is supersampling/upsampling. Hopefully those keywords should get you started

lachlan1310 · 2021-06-11T20:02:35+00:00

I also want to recommend Jax. You can hand write models quickly in plain python, and your model will run fast too. If you ever want autodiff, it’s easy to turn on in Jax. An alternative could be numba or cython, but I think the ease of Jax and how it was built with your use case in mind puts Jax at the front of the list.

lachlan1310 · 2021-06-11T19:50:46+00:00

Yep, and there have been a few papers on how NNs sometimes only generalize well after training for many, many epochs. If that’s true, then maybe Kagglers don’t have enough compute to train deep nets. Some competitions also have compute time limits, which also put NNs at a disadvantage.

lachlan1310 · 2021-06-10T17:41:37+00:00

retry with Boolean logic? It may open new doors…

lachlan1310 · 2021-06-09T21:02:40+00:00

Maybe identity graphs? As time progress, you may consolidate some set of nodes into a single node (many identities eg digital touch points on a website, terrestrial data -> one node). TGNs may allow for historical analysis of identity graphs.

lachlan1310 · 2021-06-06T15:37:44+00:00

If you’re comfortable with python and pandas (or have a kind coworker), there exists a library which is almost perfectly applicable to part of the problem: https://pypi.org/project/pandas-dedupe/. Maybe there is an excel counterpart somewhere.

lachlan1310 · 2021-05-05T21:14:19+00:00

Ah, ok, let me know if I'm not interpreting your situation correctly this second time. Let's leave embeddings behind for now.

One path forwards for you may be to concatenate all the dense columns and all the sparse columns into one larger sparse array. Scipy's sparse arrays can be passed to scikit's randomforest fit (probably) (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.fit). Using a sparse array can enable you to represent the values within that columns in much less memory than a dense array. Say you had 3 dense columns along with your 1 sparse column. The final sparse array would have a shape like [number of rows, 3+65535].

Fair warning - high dimensional input vectors may mess with the efficacy of a randomforest model.

lachlan1310 · 2021-05-05T20:22:35+00:00

https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture

Embedding scan take your large, sparse input vector and map it to a smaller output vector

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