Scikit Learn, every mention
23 scenes across 7 shows · ← back to Scikit Learn
tap a year for its mentions
the MAD Podcast 19
Latent Space 3
the Y Combinator Startup Podcast 3
the a16z Podcast 2
20VC 1
WTF is with Nikhil Kamath 1
TBPN 1
every year every show the MAD Podcast 19 the Y Combinator Startup Podcast 3 Latent Space 3 the a16z Podcast 2 WTF is with Nikhil Kamath 1 TBPN 1 20VC 1
Verbatim, from the transcripts: passages where Scikit Learn comes up on the MAD Podcast, the Y Combinator Startup Podcast, Latent Space, the a16z Podcast, WTF is with Nikhil Kamath
Neural Computers, GameStop’s $55B eBay Offer | Diet TBPN
- ▶ 6:18 John Coogan It could have used, used pandas or scikit-learn.
François Chollet: Why Scaling Alone Isn’t Enough for AGI · Y Combinator
- ▶ 53:39 François Chollet There was this big focus on usability, and this was inspired by scikit-learn. 3 times in the scene
Building AI Systems You Can Trust
- ▶ 17:49 Scott Clark Maybe every individual data scientist was like, I'm going to spin up scikit-learn, and I'm going to have all my data locally, and I'm going to have some model that works incredibly well for rapid prototyping and exploration. 2 times in the scene
Nikhil Kamath ft. Perplexity CEO, Aravind Srinivas | WTF Online Ep 1. · Nikhil Kamath
- ▶ 5:09 Aravind Srinivas scikit-learn, which was a very famous machine learning library, and it just randomly tried all these algorithms, uh, mix and match them.
[Paper Club] BERT: Bidirectional Encoder Representations from Transformers
- ▶ 34:39 unnamed speaker And then to do the classification, he just uses a basic logistic regression model from scikit-learn.
[Paper Club] SWE-Bench [OpenAI Verified/Multimodal] + MLE-Bench with Jesse Hu
Aravind Srinivas:Will Foundation Models Commoditise & Diminishing Returns in Model Performance|E1161 · 20VC with Harry Stebbings
- ▶ 2:03 Aravind Srinivas And I go and, um, check out this library called scikit-learn.
LLM Asia Paper Club Survey Round
- ▶ 28:18 unnamed speaker So if for folks that are not too familiar with this, you can go to the scikit-learn documentation and you give an example of calibration of how you can calibrate some scores to become more like probabilities.
A Conversation with Chris Wiggins - Author of "How Data Happened"
- ▶ 20:55 Chris Wiggins That said, there is also a lot of work being done in AWS, and plenty of developer work happening on Amazon's Cloud, um, so the data stack is, in my team, the data stack is SQL and scikit, and occasionally Go, so it's scikit-learn is, is a…
Fireside Chat: Nick Schrock (Founder & CEO, Elementl) with Matt Turck (Partner, FirstMark)
- ▶ 17:00 Nick Schrock They want to use tools like pandas scikit-learn.
Fireside Chat: Dave Burgess (Head of Data Engineering, Pinterest) w/ Matt Turck (Partner, FirstMark)
- ▶ 16:07 Dave Burgess We use PyTorch, scikit-learn.
Fireside Chat: Wes McKinney (Founder & CEO, Ursa Computing) with Matt Turck (Partner, FirstMark)
- ▶ 3:00 Wes McKinney So extracting, uh, extracting features from, from data sets and kind of all of that, that munging and, and, uh, and, uh, data work that that's needed before you can feed the data into a machine learning model, you know, TensorFlow…
- ▶ 6:08 Wes McKinney We needed to be able to do machine learning that came from scikit-learn, which was developed around the same time. 2 times in the scene
Fireside Chat: Alok Gupta (Head of Data Science & ML, DoorDash) with Matt Turck (Partner, FirstMark)
- ▶ 7:31 Alok Gupta A lot of it will be, um, off the shelf Python, um, ML libraries like scikit-learn, um,
Data Science Is A Literacy, Not A Job // Peter Wang, Anaconda (FirstMark's Data Driven NYC)
- ▶ 1:11 Peter Wang So, like, we have a couple of, um, a couple of the core maintainers for Pandas are full-time developers, and they're just paid to work on Pandas, and same thing with scikit-learn.
Computing On Encrypted Data // Kurt Rohloff, Duality (Firstmark's Data Driven NYC)
- ▶ 8:43 Kurt Rohloff Scikit-learn, TensorFlow, ah, machine learning models, and, ah, you know, even things as simple as regression and basic statistical type, um, analytics, and run analytics on data while it's encrypted. 2 times in the scene
Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
- ▶ 29:55 Solmaz Shahalizadeh So when the, when they are starting, we of course want them to understand the sort of basics of how classification would work, how clustering would, like, supervise, unsupervised, that, but many of these things are, like, openly available,…
The Launch of Dataiku 5 // Florian Douetteau, Dataiku (FirstMark's Data Driven NYC)
- ▶ 21:18 Florian Douetteau Meaning we provide an, I think it's interesting to have an, um, a smooth transition pass from one framework to another, from scikit to TensorFlow from, and whatever else comes next.
Building an Operating System for AI // Diego Oppenheimer, Algorithmia (FirstMark's Data Driven)
- ▶ 4:49 Diego Oppenheimer Maybe you're using scikit-learn.
- ▶ 15:30 Diego Oppenheimer Maybe I start in Python and scikit-learn.
Lukas Biewald, CrowdFlower // Enriching Your Data (Hosted by FirstMark Capital)
- ▶ 1:33 Lukas Biewald This is a sentiment task using scikit-learn with Naive Bayes, a maximum entropy model, and an SVM. 2 times in the scene
Chris Wiggins, NY Times // Data Science at The New York Times (Hosted by FirstMark Capital)
- ▶ 18:07 Chris Wiggins We use a lot of Python and scikit-learn. 2 times in the scene
- ▶ 26:24 Chris Wiggins So, um, one thing that I think we all can, you know, sort of raise the bar in our understanding of data literacies is to say, perhaps having a data literate society doesn't mean that everybody learns to code and that everybody knows…