Towards Data Science

You might have come across the sigmoid function while learning machine learning as it is used in logistic regression and also in neural networks as an activation function. Recently, I explained backpropagation in a three-part series. After that, I wanted to learn more about vanishing and exploding gradients. While learning about them, I came across the sigmoid function and thought, why not first understand this function from the basics? I wanted to understand how we get the equation and the intuition behind its mathematical structure before applying it. The equation may look simple, but when I started exploring it from the basics, beginning with the number 'e', I learned few things. That led me to write this article, where I explore the mathematical structure and intuition behind the sigmoid function in detail. Have you ever felt the same while using the sigmoid function? Then, this article might be useful for you. Thanks to Towards Data Science for publishing this. https://lnkd.in/gEFpzruP I would love to hear your thoughts!

I just published two new articles explaining how to add a proper backend for my LangGraph-based AI agent and how to test it with Streamlit UI and Docker. The first one focuses on how to set up persistence and state management aligned with agent architecture. The second one is more about testing, which shows how to add a Postgres database for long-term memory, containerize the setup, and build a Streamlit UI for local interactive testing. Towards Data Science https://lnkd.in/dTuYypGr https://lnkd.in/d4egsK9j

𝗔 𝗵𝘂𝗺𝗮𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽 𝗰𝗮𝗻 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝗸𝗲 𝗮𝗻 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 𝗹𝗲𝘀𝘀 𝘀𝗮𝗳𝗲. Sounds wrong, right? But there’s a subtle problem with putting a human approval step in front of every action: when everything needs attention, eventually 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝗴𝗲𝘁𝘀 𝗿𝗲𝗮𝗹 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻. The interesting part isn’t whether humans should be involved. 𝗜𝘁’𝘀 𝗳𝗶𝗴𝘂𝗿𝗶𝗻𝗴 𝗼𝘂𝘁 𝘄𝗵𝗲𝗻 𝘁𝗵𝗲𝗶𝗿 𝗶𝗻𝘃𝗼𝗹𝘃𝗲𝗺𝗲𝗻𝘁 𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝘁𝗵 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗿𝘂𝗽𝘁𝗶𝗼𝗻. In my latest Towards Data Science article I explored a few questions which I found surprisingly important while working through this:  • Should a harmless action really wait behind a risky one?  • What makes an AI action actually worth escalating?  • Can we preserve human oversight without making humans the bottleneck?  • And perhaps most importantly: what happens when the approval queue itself becomes a safety risk? Article link in the first comment. #GenAI #AIEngineering #LLMEvaluation #AgenticWorkflows #HumanInTheLoop #MLOps

You don’t have to sacrifice model quality to increase token generation speed. See how DFlash speculative decoding on #IntelXeon processors delivered nearly 4x the autoregressive throughput with Qwen3.5-9B at concurrency 1. Learn more about running DFlash in vLLM in our blog post with Towards Data Science: http://ms.spr.ly/6043aPQHJ

The real potential of coding agents may extend well beyond the code editor. Eivind Kjosbakken explains how to use them for practical tasks that have nothing to do with programming.

AI agents become more valuable when they can handle the messy details of real customer interactions. Soner Yıldırım shows how a LangGraph agent can collect information, manage state, and streamline a booking workflow.

"Unlike optimality cuts, which improve the approximation of the operational cost, feasibility cuts restrict the master to decisions that admit at least one feasible operational solution." Luis Fernando Perez Armas, Ph.D. continues to explore Benders decomposition in his latest deep dive.

"As soon as a prediction triggers an automated action, hidden uncertainty becomes operational risk." Don't miss Mahe Jabeen Abdul's insightful deep dive on leveraging Bayesian guardrails for facilitating AI decision-making.

Regular, junk, and degenerate dimensions: do you know what they are and how to use them in a star schema? Follow along Salvatore Cagliari's guide to learn about these key concepts in dimensional modeling.