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#datascientist

2 posts2 participants1 post today
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𝓖ö𝓷𝓰𝓾 𝓓ó𝓽𝓽𝓲𝓻

26 Tools for Defending Democracy

In a handbook, scholars from the US and Europe offer advice on how people in academia can combat emerging authoritarianism – graded by risk level. This is a worthwhile read, and not just for researchers.

Via @netzpolitik_feed

netzpolitik.org/2025/anti-auto

netzpolitik.org · Anti-Autokratie-Handbuch: 26 Werkzeuge zur Verteidigung der DemokratieIn einem Handbuch geben Wissenschaftler*innen aus den USA und Europa Ratschläge, wie sich Menschen im akademischen Betrieb gegen aufkeimenden Autoritarismus stemmen können – abgestuft nach Risiko-Level. Das ist nicht nur für Forschende lesenswert.
#research#Democracy#academia
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Sarah Lea

Reinforcement Learning doesn’t tell you what’s right.
It only tells you how good your choice was.
No feedback on what to do. Only on how it went.

:blobcoffee: Example: A multi-armed bandit (like a slot machine with several levers). You don't know which lever is the best - you can only find out by trying it out. Exploring means giving up a known reward (from exploitation) — in hopes of finding a better one.

This balance between exploration and exploitation is the central dilemma in reinforcement learning.

:blobcoffee: A simple strategy is ε-greedy:
→ In 90% of cases you take the best known action
→ In 10% of cases, you try a different one by chance

In simulations, ε-greedy methods perform better in the long term than pure greed (always take the supposedly best) - because they master the “explore-exploit trade-off”.

#ReinforcementLearning#ML#KI
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Sarah Lea

What does a baby learning to walk have in common with AlphaGo’s Move 37?

Both learn by doing — not by being told.

That’s the essence of Reinforcement Learning.

In my latest article, I explain Q-learning with a bit Python and the world’s simplest game: Tic Tac Toe.

-> No neural nets.
-> Just some simple states, actions, rewards.

The result? A learning agent in under 100 lines of code.

Perfect if you are curious about how RL really works, before diving into more complex projects.

Concepts covered:
:blobcoffee: ε-greedy policy
:blobcoffee: Reward shaping
:blobcoffee: Value estimation
:blobcoffee: Exploration vs. exploitation

Read the full article on Towards Data Science → towardsdatascience.com/reinfor

Towards Data Science · Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python | Towards Data ScienceInspired by AlphaGo’s Move 37 — learn how agents explore, exploit, and win
#Python#ReinforcementLearning#ML
Public
remoteone

Unlock Insights and Drive Growth with a Data Scientist! 📊🚀

Data is the key to making informed decisions. A data scientist can help you uncover valuable insights from your data to improve business strategies and drive growth. From predictive analytics to machine learning models, a data scientist brings your data to life! Ready to harness the power of your data? Find your perfect data scientist today at RemoteOne and start making smarter decisions!

remoteone.co/roles/data-scient

#DataScientist#DataAnalytics#BusinessGrowth
Public
Sarah Lea

What does your Spotify account say about your music taste?
I was curious – so I built a little app to find out. Using Python, Streamlit and Plotly, I visualised my top tracks, favourite genres and the release years of the songs I listen to most.

What you need:
:blobcoffee: Access to the Spotify Web API
:blobcoffee: A bit of Pandas for analysis
:blobcoffee: A few lines of code with Plotly
:blobcoffee: Streamlit to build and run the app

→ The result? A personal, interactive music year in review 🎶

I put together a step-by-step guide (beginner-friendly) including code, screenshots, and how to deploy the app on Streamlit Cloud.

👉 Check out the full article here: bit.ly/3SF3VTw

#python #programming #technology #streamlit #spotify #Datavisualization #datascience #datascientist #opensource #api

Friends-Link: medium.com/data-science-collec

Data Science Collective · Visualize Your Spotify Data with Python and StreamlitBy Sarah Lea
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