roosbeh

[Harrison Chase @hwchase17]

One of the better agentic AI courses I've seen

Nearly 10 hours of great content. Covers LangChain, LangGraph, RAG, deepagents, guardrails, and more

Any other good Lang* resources out there for folks who are interested in learning?

https://t.co/OXNPMeGiyd

[Matt Harrison @__mharrison__]

Pandas makes it so easy to pivot data...

Oh, that plot looks a little hard to read.

Let's add a 7 day rolling mean (with one line).

Much better! 🐼💪

[Kirk Borne @KirkDBorne]

This FREE 280-page PDF report from @JPMorgan provides an excellent framework for #MachineLearning #AI and #BigData #DataScience investors, including an overview of types of alternative data and a brilliant tutorial on #ML methods to analyze the data:
https://t.co/YXQ9YEhxA2

[Alex Smola @smolix]

Join us for CS239P https://t.co/7iCn8OaRZ4 on Practical Machine Learning with Quingquing Huang and @mli65 this fall. AutoML, Distillation, Distributed training, Model Serving, Distribution Tests, Fairness and much more. All online, videos, and slides for free!

[Mike Tamir, PhD @MikeTamir]

Transformer models - Hugging Face Course https://t.co/TieFAvR5Fl
#AI #MachineLearning #DeepLearning #DataScience

[roosbeh @roosbehnowrouzi]

Summary of encoding techniques #DataAnalytics #datascience

[Yael Grushka-Cockayne @grushkay]

Everything you ever wanted to know about forecasting in a single manuscript. "Forecasting: theory and practice" #forecasting https://t.co/otpAXVDJZl https://t.co/IBnAex3LUo

[Sean Kross @seankross]

I am so excited to announce that my new paper “Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia” has been accepted to #CSCW2021! You can read it here: https://t.co/BqNKcn7Kxf

[Emily Riederer @EmilyRiederer]

Too many broadly useful stats methods are masked in domain-specific language. In my new pair of posts, I discuss formula-free #causalinference design patterns to help data analysts recognize frameworks as they encounter them in everyday work

https://t.co/fV2VGLpfju

1/3

[roosbeh @roosbehnowrouzi]

@Wearewewill @lisachwinter I am

[roosbeh @roosbehnowrouzi]

@LynAldenContact What’s slow and steady in investing?

[Made With ML @MadeWithML]

We love seeing these amazing projects shared directly by the authors and watching the community-curated collection of tutorials and libraries grow with these quality additions!

More on topic modeling:
https://t.co/dBF1GsbT6u

More on transformers:
https://t.co/WymqY8hXdw

[Made With ML @MadeWithML]

🏆 Today's trending posts are surveys on interpretability:

- Principles & Practice of Explainable ML:
https://t.co/bjgBo6u2hi

- Explainable AI for NLP:
https://t.co/iatX8UJNzS

- Curated surveys:
https://t.co/RdRljGkiFD

- Curated interpretability posts:
https://t.co/tPixcRIE16

[Mara Averick @dataandme]

🤯 Welp, @Datatitian just blew my mind (it even works in link preview)!
"How to Turn Your ggplot2 Visualization into an Interactive Tweet" https://t.co/MArL4PPBYJ ht @blaine_bateman #rstats #dataviz

[Kirk Borne @KirkDBorne]

#Python Examples of Popular #MachineLearning #Algorithms with Interactive #Jupyter Demos and the Background Math Explained: https://t.co/5dOvVcDGS4 by @Trekhleb

#AI #DAIFE #DeepLearning #DataScience #BigData #coding #DataScientists #Mathematics
HT @pierrepinna 👈Thanks!

[ph_ @jpatrickhall]

New to awesome-#machinelearning-interpretability meta-list: a blueprint for a more human-friendly ML - train interpretable models, explain them, debug them, and test them for fairness cc: @Navdeep_Gill_ @MaverickPramit (link: https://t.co/vdT9bBS4LY) #FATML #XAI #DataScience

[Bradley Boehmke @bradleyboehmke]

2 decent papers on auto-#MachineLearning :

- A review on automated selection procedures (https://t.co/XJGWxEp0E4)
- An approach for creating multiple hyperparameter defaults, a great way for organizations to provide initial direction for their analysts (https://t.co/2xgwV5co3s)

[👋 @Cmrn_DP]

lifelines v0.15.0 is available! Lots of new features & models, 100% faster fitting, and inverted y-axis KM curves, and more information on model results. Summary here: https://t.co/7kxUeGh7Sf #python

[Christoph Molnar 🦋 christophmolnar.bsky.social @ChristophMolnar]

The first version of my online book on Interpretable Machine Learning is out!

https://t.co/s4tyZz3f3B

I am very excited to release it. It's a guide for making machine learning models explainable.
#interpretableML #iml #ExplainableAI #xai #MachineLearning #DataScience