Implementing AI Agents across LangChain, LlamaIndex, AWS, Gemini, AutoGen, CrewAI and Agent protocol - by Han HELOIR, Ph.D. ☕️ buff.ly/3wjnKsb

'in this story, I experimentally compare ξ to the mutual information-based coefficient R along a variety of properties one would like a nonlinear correlation measure to satisfy.'

Vadim Arzamasov explores the benefits of a nonlinear correlation measure. buff.ly/4a1XHnc

We're thrilled to share Vu Trinh's first TDS article: a detailed walkthrough of the Dataflow model paper, and an analysis of its lasting impact on data-processing workflows. buff.ly/3QqJqJT

By taking a close look at the evolution of human intelligence, could we gain a better understanding of AI's potential — and limitations?

Stephanie Shen's latest deep dive explores a fascinating question at the intersection of neuroscience and technology. buff.ly/3JEDtVV

How can we increase trust in our prediction models?

We quantify the model's uncertainty.

In my latest article in Towards Data Science I show you how easy it can be. (Spoiler: it just takes 3 lines of code)

If you'd like to expand your knowledge of Gradio, Thomas Reid walks us through the open-source package's more advanced layout options. buff.ly/4a5J7Lb

What can a series of tic-tac-toe games tell us about how Llama 2's performance compares to that of new arrival Llama 3? Dmitrii Eliuseev sets out to find out with the aid of some Python and Llama-CPP. buff.ly/3WdksRO

'... while machine learning models offer significant advantages, their increasing complexity poses challenges regarding explainability, interpretability, and compliance...'

Explaining complex models to business stakeholders by Frida Karvouni buff.ly/3WmCrVY

My latest geospatial data + python tutorial on Towards Data Science - 𝐏𝐮𝐛𝐥𝐢𝐜 𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭 𝐀𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧

𝐑𝐞𝐚𝐝 𝐢𝐭 𝐡𝐞𝐫𝐞 : towardsdatascience.com/public-transpo… 

+

𝐈𝐧𝐭𝐫𝐨 𝐭𝐨 𝐆𝐓𝐅𝐒 𝐝𝐚𝐭𝐚 : towardsdatascience.com/quantifying-tr…

My latest Medium article has been published on
Towards Data Science 🚀
In this article I demonstrate how to use Hydra and
MLflow , two open-source tools, to launch experiments in a structured way.
I'd like also to thank Deepnote for providing their great data science tool. 👇

In this article, Elaine explores 6 reasons why 85% of AI projects fail:

1️⃣ AI systems don’t solve the right problem
2️⃣ AI innovation gap
3️⃣ AI systems can’t achieve good enough performance and are not useful
4️⃣ People miss low-hanging fruit
5️⃣ ...

buff.ly/3xRMLLm

For a comprehensive explainer on what temperature means in the context of LLMs, how it works, and how to use it effectively, don't miss Mike Cvet's excellent deep dive. buff.ly/4aSdczg

If you'd like to tinker with Meta's latest LLM, Llama 3, while also learning the ins and outs of relation extraction, don't miss Silvia Onofrei's clear and actionable guide. buff.ly/4aSx99a

In an accessible, hands-on primer, Hans Christian Ekne explains what one-hot encoding is, how it works, and how to implement it effectively in both Python and R. buff.ly/3w1Vlqz

What do you do if you need hundreds of clipped satellite images of a specific area of interest for your geospatial-data project? Mahyar's new tutorial shows how you can accomplish that with a short script. buff.ly/4diT2Ae

Curious about Llama 3? Eivind Kjosbakken offers a simple approach to running Llama 3 locally on your computer, runs different tests to understand its capabilities, and gives his thoughts on the model's performance. buff.ly/3waTuQ9 . buff.ly/3waTuQ9

Learn how double machine learning and linear programming can be used to optimize treatment strategies — Ryan O'Sullivan offers a full case study (complete with Python code you can experiment with on your end). buff.ly/3vZtQxS

Leveraging the power of Hydra and MLflow, Marcello Politi shows how you can build more efficient pipelines for hyperparameters tuning. buff.ly/3UeWmDD

What is type widening? Why does it matter? Vitor Teixeira's beginner-friendly guide explains the potential use cases for this new Delta Lake feature. buff.ly/3y01rZ6

What's the most effective way to learn and practice your data science skills? Egor Howell shares the strategies he's come to rely on and wishes he'd known about sooner. buff.ly/3JDQNtl