ian, to random
@ian@phpc.social avatar

Rule of : be your own best competitor. If your ML model is your key differentiator, once you ship it start working on a new and improved version.

ian, to random
@ian@phpc.social avatar

Rule of : if it doesn't work in production it doesn't exist.

ian, to random
@ian@phpc.social avatar

K-Fold Cross Validation: re-split your training vs. test data a bunch of times (usually 5x) to see whether your model is valid or whether you wound up in one of the naturally-occurring clusters in random data.

ian, to random
@ian@phpc.social avatar

Only use neural networks when, after cleaning the data up, you have more than 250 variables.

Issue is, neural nets can't be explained fully, so at least in the UK you can't use neural nets because you can't explain the algorithm.

There's also the issue of "randomly jiggling the algorithm" that neural nets use to avoid local maxima.

Just like blockchain, if you think you need to use a neural net, no you don't.

ian, to random
@ian@phpc.social avatar

If your model is 0% sure or 100% sure of a thing, you did something very wrong. Split your test and training data (usually 80/20).

ian, (edited ) to random
@ian@phpc.social avatar

Have a favorite data science model, but try that in competition with another model.

Linear equations normally work well because you're either dealing with people or things that depend on people. But also check against a nonlinear model, and pick which one models the data better.

ian, (edited ) to random
@ian@phpc.social avatar

You can't add apples and oranges; match units on your calculations when doing data science. Normalize your units like you're back in grade school.

ian, (edited ) to random
@ian@phpc.social avatar

Exploratory data analysis

  • Understand the variables
  • Handle missing values (in a documented, well-explained way)
  • Outlier detection
  • Univariate analysis
  • Bivariate analysis

If you have two highly correlated variables, pick the one that provides more information and discard the other/noisier one.

ian, (edited ) to random
@ian@phpc.social avatar

"If you can't explain it to a six-year-old, you don't understand it."

  • Richard Feynman
  • Gary Short

ian, (edited ) to random
@ian@phpc.social avatar

All AI/ML works on these types of numbers:

  • Categorical
  • Ordinal
  • Numberic
  • Ratio

"on a scale of 1 to 5 how happy are you"...just because you replace "unhappy" with 2 doesn't make it numeric data, any more than replacing it with "potato"

ian, (edited ) to random
@ian@phpc.social avatar

Rule 3 of data science: You can only do maths with numbers

ian, (edited ) to random
@ian@phpc.social avatar

The "six degrees of separation" statement...turns out not to be a myth. Microsoft did a similar study with user accounts/email addresses and the number was 6.2.

ian, (edited ) to random
@ian@phpc.social avatar

If your relational database is a sparse matrix (lots of nulls), it should've been thrown into a document database

ian, (edited ) to random
@ian@phpc.social avatar

For structured data, the schema is important when the data is written. For unstructured data, the schema is important when the data is read.

ian, (edited ) to random
@ian@phpc.social avatar

All ML is AI. Not all AI is ML. AI can appear to be intelligent without actually being intelligent.

ian, (edited ) to random
@ian@phpc.social avatar

"You haven't discovered $#!& if your p is not < 0.05"

ian, (edited ) to random
@ian@phpc.social avatar

Time to do the things with Gary Short at

"Who's gonna be scared of Python code? Who's gonna be scared of my Python code?"

christopherd, to Ukraine
@christopherd@mastodon.nz avatar

Use of data to explain things. Despite the subject matter, it's pretty cool.

https://youtu.be/_r-zQx05Rfk

JamesDBartlett3, to tableau
@JamesDBartlett3@techhub.social avatar

I'm immensely proud of my wife, @likeawednesday.

She worked tirelessly on her at for two grueling years during the pandemic, and now all of her hard work has finally paid off. She just accepted a new position with , starting later this month!

Erin will be working primarily with , whereas I'm a dyed-in-the-wool , so we'll soon have a bitter in our household. ! 🤬😏

Kidding aside, every tool has its own and , and I know that Power BI can do certain things that Tableau can't do, but I'm also sure that the reverse is true as well.

Erin and I frequently talk about the we use at work during our lunches and evening walks, and I'm genuinely looking forward to more about from her, as we continue honing each other's minds like iron sharpening iron. ⚔️

eaton, to ArtificialIntelligence
@eaton@phire.place avatar

Okay, and friends. I’m poking around for the “right way” to approach a problem: I want to calculate the overal homogeneity of many short snippets of text (phrases and sentences), and many large spans of text (500-1500 word documents).

mia, to ai
@mia@hcommons.social avatar

Free in London this Saturday afternoon? Want to mosey around the British Academy and hear from a range of excellent speakers on many important subjects of our times?

Check out the British Academy Summer Showcase 2023! https://www.thebritishacademy.ac.uk/events/british-academy-summer-showcase-2023/

I'm chairing a session on 'ChatGPT, AI, and the future' in the garden, 2-3pm, with Tim Gordon (Co-founder, Best Practice AI) and Hetan Shah (CEO, British Academy)

'Much consideration has been given to how machine learning is influencing our lives, and what it means for the near future. This panel will consider ChatGPT’s current influence in research, and how it might be a tool for copyright theft, content creation, knowledge-sharing or misinformation.

This panel of experts will critically examine whose voices are being heard in the discourse around AI, what choices we must make about how it is implemented, and which technologies are bringing genuine value to world of education and research.'

mirela, to privacy

Personal: I am very happy to announce that I have accepted a tenure-track Assistant Professor position at the University of Groningen. Looking forward to further collaborations, and am glad to continue working within the Information Systems Group at the Bernoulli Institute.

@academicchatter get in touch if you are working on , , , , , @academicsunite

veit, to programming
@veit@mastodon.social avatar

The talk by @leahawasser on @pyOpenSci at @pyberlin was very interesting. For those who could not be there, the presentation slides are now published: https://python.berlin/en/latest/pub/resources/index.html

veit, to programming
@veit@mastodon.social avatar

Final preparations for the Meetup tonight with a talk by @leahawasser on @pyOpenSci. I would be happy to see you. Sign up: https://www.meetup.com/python-users-berlin-pub/events/293638071/

fosslife, to ai
@fosslife@fosstodon.org avatar

2023 Future of Jobs Report from World Economic Forum says demand for AI and machine learning specialists will grow by 40 percent https://www.fosslife.org/global-outlook-ai-jobs-and-skills

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