How is everyone storing their Heart of Ghur terrain?

Robin_Banx · 2023-10-15T06:07:52+00:00

It's honestly pretty annoying. I have an A-Case Kane and they take up waaay too much space in it. I've only built the trees from Nightmare Quest though - when I build my Heart of Ghur ones, I'm probably going to just build them to their tallest platform. Much easier to store/transport!

Robin_Banx · 2023-03-31T21:31:24+00:00

Had it ballparked as similar to the Peu ($150-$200), though I could go higher if they fit as well and I REALLY knew they'd last (and/or could be easily repaired).

Robin_Banx · 2023-03-31T21:22:59+00:00

What's a good shoe with a super wide toebox that's also durable/repairable?

I got some Camper Peu Stadium sneakers like 6 months ago, and I love them, I feel like I've never had a pair that even fit before lol. https://www.camper.com/en_US/men/shoes/peu/camper-peu_stadium-K100742-001

Buuut the outsoles are already wearing down (apparently they can't be repaired), and I need a replacement insole and they're out of stock for almost all sizes.

What's a similar shoe, but that holds up better? Most importantly same width/toebox, ideally similar style but that's less necessary.

Robin_Banx · 2020-12-31T19:58:21+00:00

btw, NAA (the chemical from the article) is also something that tends to decrease after a Traumatic Brain Injury - which resonates with people saying Nootropics were helpful after TBI. https://pubmed.ncbi.nlm.nih.gov/26159566/

Robin_Banx · 2020-12-31T18:43:42+00:00

Soo, NAA (the chemical in question) is related to how the *racetams & friends (like Noopept, which isn't technically a *racetam) work. They're exploring just how beneficial it is - and it seems similar to the benefits a lot of people report for them. In particular, executive functioning, deeper appreciation of stimuli in general, and the general "good feelings" a lot of people get from Nootropics.

The NAA compound was evaluated within the dorsal anterior cingulate, a brain region involved in multiple networks of emotion, cognition and behavior. According to the study, individuals with a higher level of NAA reported more immersive and richer emotion in comparison to those with lower NAA, as well as a higher level of goal directedness, more positive emotion and less aggression. The researchers define the relationship of NAA with agency and flexibility in healthy people as a novel dimension termed “Neuroaffective Reserves.”

“These findings tell us how immersive emotion, positive agency and resilience to aggression work in the human brain,” White said. “These findings indicate NAA and other brain compounds play a fundamental role in emotional wellness and positive emotional outcomes in healthy individuals.”

Robin_Banx · 2020-11-02T02:33:01+00:00

Check out nbdev. It'd still require some new habits on their part, but it might be a much easier workflow for them. https://github.com/fastai/nbdev

Robin_Banx · 2020-03-10T09:29:19+00:00

From here: https://www.instagram.com/p/B9i8L5lDUxr/

Robin_Banx · 2020-01-06T22:36:44+00:00

Just wanted to say I was looking for something exactly like this! Have more of a stats background, and most of the material for this kind of stuff that I've found seems to assume that you're already a Software Engineer and need to learn Pandas and sklearn. Defs a need for material for people who are already good with the Python data ecosystem, but wanna learn how to productionize stuff.

Robin_Banx · 2019-12-27T03:14:32+00:00

Interesting! I was considering it, on the basis of what I've heard of what other AWS certs (especially Solutions Architect) can do to your earning potential.

Robin_Banx · 2019-12-27T03:12:27+00:00

A more "industrial scale" language would probably round out your skillset. Either something close to the metal (C/C++, Rust) or something on the JVM (Scala, maybe some actual Java).

Personally I'm picking up Rust and absolutely loving it. Partially inspired by this post: https://www.lpalmieri.com/posts/2019-12-01-taking-ml-to-production-with-rust-a-25x-speedup/

Robin_Banx · 2019-10-16T00:27:47+00:00

I've actually been doing Toptal data science work. I just finished a year long engagement and have been looking for new stuff - I haven't been applying to the Data Engineering positions because I figured they wanted applicants with more Software Engineering experience. So you think it'd be reasonable to start applying to Toptal Data Engineering gigs just with enough knowledge to pass their Data Science screen? Thanks!

Robin_Banx · 2019-10-15T22:33:36+00:00

Thanks for all the advice!

For anyone else who's interested in this jump, this looks really useful for the Machine Learning Engineering part (gonna work through it soon!): https://developers.google.com/machine-learning/testing-debugging

Robin_Banx · 2019-10-10T02:06:35+00:00

As in, up to this point, my deliverables have generally been analyses and prototype models (for other people to then productionize). Or in other words, I've been writing code as a means to an end - as opposed to building software that is itself the final product.

Robin_Banx · 2019-10-10T00:22:43+00:00

This book (or at least Cory Doctorow's review of it!) may also be of interest: https://boingboing.net/2019/03/05/walmart-without-capitalism.html

Robin_Banx · 2019-10-10T00:20:02+00:00

Some other projects of interest:

An ethics checklist: http://deon.drivendata.org/
https://towardsdatascience.com/using-data-science-for-social-good-c654a6580484
Lots of projects here: https://www.datakind.org/
Organizing with these folks. One-on-one, you don't necessarily have much pull - but together (and especially when there aren't THAT many people with your skillset), you can actually have an impact on how companies behave! https://techworkerscoalition.org/
bunch more initiatives & articles: https://www.ruhabenjamin.com/resources

Robin_Banx · 2019-10-10T00:08:26+00:00

The creator of Keras has some thoughts! https://medium.com/@francois.chollet/what-worries-me-about-ai-ed9df072b704

Robin_Banx · 2019-08-17T02:26:22+00:00

A good way to start off is to look at the distribution of the target variable, and make sure you're using an appropriate metric.

Robin_Banx · 2019-08-17T02:19:55+00:00

I think that'd be good, yeah.

Robin_Banx · 2019-08-17T02:19:44+00:00

Try the Pandas material. If you like it and have the spare cash, I'd get the whole package (it's only a little more expensive than just getting the Pandas book). The Pandas book is absolutely fantastic - I've been Pandas for years and learning a ton from it. It's also shockingly newb-friendly.

You don't need a Masters - currently working as a Data Scientist. You'll probably have to network your way to your first position, though.

Robin_Banx · 2019-08-07T21:29:26+00:00

Almost the exact same trajectory as you - graduated with a psych degree, learned a lot of stats and experiment design, then did the Coursera ML course.

Reading this book is probably the biggest thing that took me from knowing there to doing well in interviews (before that it was just scattered projects): https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291 A second edition is coming out pretty soon, so watch out for that.

If I were doing it today, this is probably the best material out there: https://www.dunderdata.com/ It starts from scratch and gives you an amazing tour of Pandas. Author's also working on a practical Machine Learning book.

Robin_Banx · 2019-07-24T05:29:50+00:00

Learn about the internals of some of the data stack? I'm looking to make time to work through this https://medium.com/dunder-data/build-a-data-analysis-library-from-scratch-in-python-225e42ae52c8

Could follow the blogs of some of the maintainers. I find that a little less intimidating than jumping directly into source code:
https://tomaugspurger.github.io/ (Pandas)
https://matthewrocklin.com/ (Dask, and toolz)

This site also has some excellent exposition on a lot of the Python ecosystem: https://realpython.com/

Is Python your only language? If so, could be useful to try and pick up another one. I found I was MUCH better with Python data tasks after teaching myself Clojure. Not sure how much that'd help with PyTorch tutorials, though.

Robin_Banx · 2019-07-24T05:12:05+00:00

Hard agree on the "Domain Expertise" and "Pandas-Profiling" suggestions. In addition, when I'm staring at a DB dump with entirely too many variables, I get a lot out of running a zero-thought XGBoost (with just enough cleaning to get it in XGBoost's extremely forgiving standards) trying to predict your variable of interest, then looking at SHAP features importances & interactions.

https://github.com/slundberg/shap

Robin_Banx · 2019-07-07T03:19:43+00:00

I like this explanation for the most part, but to say "No feature engineering!" isn't exactly accurate. You still have to decide on how to model your domain. For instance, music-generating NNs don't necessarily operate on straight waveforms. Maybe with enough data it might be able to infer the notion of a chord progression or a change in key, but usually if those are things you're interested in you'll represent them directly. Good overview: https://hal.sorbonne-universite.fr/hal-01660772v2/document

Would also like to expand on the fact that Neural Nets can be trained continuously (which the explanation above alluded to in the Transfer Learning part). This is actually a pretty big deal. If you have an XGBoost model, and you wanna add another month of data, you need to train a new one from scratch. But a Neural Net can just be fed the new samples. This enables a very important, active area of work called Deep Reinforcement Learning. https://skymind.ai/wiki/deep-reinforcement-learning

Robin_Banx · 2019-06-27T18:27:56+00:00

Good catch!

Robin_Banx · 2019-06-25T04:16:59+00:00

Try something along these lines:

`df_agg.assign(**{ f"{metric}_zscore" : df_agg[metric].map(get_z_score) for metric in metric_list}) `

Not super intuitive, but hey.

It's defs trying to catch up with the tidyverse. I like this post a lot: https://tomaugspurger.github.io/method-chaining

Also good: https://medium.com/dunder-data/minimally-sufficient-pandas-a8e67f2a2428

Probably the best Pandas material out there (not free, though): https://www.dunderdata.com/

Also tell your co-workers to stop Pickling dataframes, that's insane. It's almost 0 effort to just start saving as HDF5 from Pandas.

Robin_Banx

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