The Machine Learning “Advent Calendar” Day 12: Logistic Regression in ExcelTowards Data Science In this article, we rebuild Logistic Regression step by step directly in Excel.
Starting from a binary dataset, we explore why linear regression struggles as a classifier, how the logistic function fixes these issues, and how log-loss naturally appears from the likelihood.
With a transparent gradient-descent table, you can watch the model learn at each iteration—making the whole process intuitive, visual, and surprisingly satisfying.
The post The Machine Learning “Advent Calendar” Day 12: Logistic Regression in Excel appeared first on Towards Data Science.
In this article, we rebuild Logistic Regression step by step directly in Excel.
Starting from a binary dataset, we explore why linear regression struggles as a classifier, how the logistic function fixes these issues, and how log-loss naturally appears from the likelihood.
With a transparent gradient-descent table, you can watch the model learn at each iteration—making the whole process intuitive, visual, and surprisingly satisfying.
The post The Machine Learning “Advent Calendar” Day 12: Logistic Regression in Excel appeared first on Towards Data Science. Read More
How to Write Efficient Python Data ClassesKDnuggets Writing efficient Python data classes cuts boilerplate while keeping your code clean. And this article will teach you how.
Writing efficient Python data classes cuts boilerplate while keeping your code clean. And this article will teach you how. Read More
AI in 2026: Experimental AI concludes as autonomous systems riseAI News Generative AI’s experimental phase is concluding, making way for truly autonomous systems in 2026 that act rather than merely summarise. 2026 will lose the focus on model parameters and be about agency, energy efficiency, and the ability to navigate complex industrial environments. The next twelve months represent a departure from chatbots toward autonomous systems executing
The post AI in 2026: Experimental AI concludes as autonomous systems rise appeared first on AI News.
Generative AI’s experimental phase is concluding, making way for truly autonomous systems in 2026 that act rather than merely summarise. 2026 will lose the focus on model parameters and be about agency, energy efficiency, and the ability to navigate complex industrial environments. The next twelve months represent a departure from chatbots toward autonomous systems executing
The post AI in 2026: Experimental AI concludes as autonomous systems rise appeared first on AI News. Read More
Decentralized Computation: The Hidden Principle Behind Deep LearningTowards Data Science Most breakthroughs in deep learning — from simple neural networks to large language models — are built upon a principle that is much older than AI itself: decentralization. Instead of relying on a powerful “central planner” coordinating and commanding the behaviors of other components, modern deep-learning-based AI models succeed because many simple units interact locally
The post Decentralized Computation: The Hidden Principle Behind Deep Learning appeared first on Towards Data Science.
Most breakthroughs in deep learning — from simple neural networks to large language models — are built upon a principle that is much older than AI itself: decentralization. Instead of relying on a powerful “central planner” coordinating and commanding the behaviors of other components, modern deep-learning-based AI models succeed because many simple units interact locally
The post Decentralized Computation: The Hidden Principle Behind Deep Learning appeared first on Towards Data Science. Read More
The One Data Analyst Role That’s AI-ProofKDnuggets And it pays $100K+ more than regular data analyst jobs.
And it pays $100K+ more than regular data analyst jobs. Read More
EDA in Public (Part 1): Cleaning and Exploring Sales Data with PandasTowards Data Science Hey everyone! Welcome to the start of a major data journey that I’m calling “EDA in Public.” For those who know me, I believe the best way to learn anything is to tackle a real-world problem and share the entire messy process — including mistakes, victories, and everything in between. If you’ve been looking to level up
The post EDA in Public (Part 1): Cleaning and Exploring Sales Data with Pandas appeared first on Towards Data Science.
Hey everyone! Welcome to the start of a major data journey that I’m calling “EDA in Public.” For those who know me, I believe the best way to learn anything is to tackle a real-world problem and share the entire messy process — including mistakes, victories, and everything in between. If you’ve been looking to level up
The post EDA in Public (Part 1): Cleaning and Exploring Sales Data with Pandas appeared first on Towards Data Science. Read More
5 Lightweight Alternatives to Pandas You Should TryKDnuggets Get started with five free Python libraries that let you analyze, filter, and process data faster than traditional Pandas.
Get started with five free Python libraries that let you analyze, filter, and process data faster than traditional Pandas. Read More
How to Design a Fully Local Agentic Storytelling Pipeline Using Griptape Workflows, Hugging Face Models, and Modular Creative Task OrchestrationMarkTechPost In this tutorial, we build a fully local, API-free agentic storytelling system using Griptape and a lightweight Hugging Face model. We walk through creating an agent with tool-use abilities, generating a fictional world, designing characters, and orchestrating a multi-stage workflow that produces a coherent short story. By dividing the implementation into modular snippets, we can
The post How to Design a Fully Local Agentic Storytelling Pipeline Using Griptape Workflows, Hugging Face Models, and Modular Creative Task Orchestration appeared first on MarkTechPost.
In this tutorial, we build a fully local, API-free agentic storytelling system using Griptape and a lightweight Hugging Face model. We walk through creating an agent with tool-use abilities, generating a fictional world, designing characters, and orchestrating a multi-stage workflow that produces a coherent short story. By dividing the implementation into modular snippets, we can
The post How to Design a Fully Local Agentic Storytelling Pipeline Using Griptape Workflows, Hugging Face Models, and Modular Creative Task Orchestration appeared first on MarkTechPost. Read More
Enabling small language models to solve complex reasoning tasksMIT News – Machine learning The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.
The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting. Read More
Towards Foundation Models with Native Multi-Agent Intelligencecs.AI updates on arXiv.org arXiv:2512.08743v2 Announce Type: replace
Abstract: Foundation models (FMs) are increasingly assuming the role of the “brain” of AI agents. While recent efforts have begun to equip FMs with native single-agent abilities — such as GUI interaction or integrated tool use — we argue that the next frontier is endowing FMs with native multi-agent intelligence. We identify four core capabilities of FMs in multi-agent contexts: understanding, planning, efficient communication, and adaptation. Contrary to assumptions about the spontaneous emergence of such abilities, we provide extensive empirical evidence across 41 large language models showing that strong single-agent performance alone does not automatically yield robust multi-agent intelligence. To address this gap, we outline key research directions — spanning dataset construction, evaluation, training paradigms, and safety considerations — for building FMs with native multi-agent intelligence.
arXiv:2512.08743v2 Announce Type: replace
Abstract: Foundation models (FMs) are increasingly assuming the role of the “brain” of AI agents. While recent efforts have begun to equip FMs with native single-agent abilities — such as GUI interaction or integrated tool use — we argue that the next frontier is endowing FMs with native multi-agent intelligence. We identify four core capabilities of FMs in multi-agent contexts: understanding, planning, efficient communication, and adaptation. Contrary to assumptions about the spontaneous emergence of such abilities, we provide extensive empirical evidence across 41 large language models showing that strong single-agent performance alone does not automatically yield robust multi-agent intelligence. To address this gap, we outline key research directions — spanning dataset construction, evaluation, training paradigms, and safety considerations — for building FMs with native multi-agent intelligence. Read More