Artificial Intelligence | Towards Data Science https://towardsdatascience.com/category/artificial-intelligence/ Publish AI, ML & data-science insights to a global community of data professionals. Mon, 15 Dec 2025 20:49:55 +0000 en-US hourly 1 https://wordpress.org/?v=6.8.3 https://towardsdatascience.com/wp-content/uploads/2025/02/cropped-Favicon-32x32.png Artificial Intelligence | Towards Data Science https://towardsdatascience.com/category/artificial-intelligence/ 32 32 The Machine Learning “Advent Calendar” Day 15: SVM in Excel https://towardsdatascience.com/the-machine-learning-advent-calendar-day-15-svm-in-excel/ Mon, 15 Dec 2025 19:41:01 +0000 https://towardsdatascience.com/?p=607912 Instead of starting with margins and geometry, this article builds the Support Vector Machine step by step from familiar models. By changing the loss function and reusing regularization, SVM appears naturally as a linear classifier trained by optimization. This perspective unifies logistic regression, SVM, and other linear models into a single, coherent framework.

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]]> Lessons Learned from Upgrading to LangChain 1.0 in Production https://towardsdatascience.com/lessons-learnt-from-upgrading-to-langchain-1-0-in-production/ Mon, 15 Dec 2025 10:30:00 +0000 https://towardsdatascience.com/?p=607893 What worked, what broke, and why I did it

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]]> The Machine Learning “Advent Calendar” Day 14: Softmax Regression in Excel https://towardsdatascience.com/the-machine-learning-advent-calendar-day-14-softmax-regression-in-excel/ Sun, 14 Dec 2025 18:12:00 +0000 https://towardsdatascience.com/?p=607910 Softmax Regression is simply Logistic Regression extended to multiple classes.

By computing one linear score per class and normalizing them with Softmax, we obtain multiclass probabilities without changing the core logic.

The loss, the gradients, and the optimization remain the same.
Only the number of parallel scores increases.

Implemented in Excel, the model becomes transparent: you can see the scores, the probabilities, and how the coefficients evolve over time.

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]]> The Skills That Bridge Technical Work and Business Impact https://towardsdatascience.com/the-skills-that-bridge-technical-work-and-business-impact/ Sun, 14 Dec 2025 14:30:29 +0000 https://towardsdatascience.com/?p=607866 In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science and AI, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Maria Mouschoutzi.  Maria is a Data Analyst and Project Manager with a strong background in Operations Research, Mechanical […]

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]]> The Machine Learning “Advent Calendar” Day 13: LASSO and Ridge Regression in Excel https://towardsdatascience.com/the-machine-learning-advent-calendar-day-13-lasso-and-ridge-regression-in-excel/ Sat, 13 Dec 2025 16:56:00 +0000 https://towardsdatascience.com/?p=607908 Ridge and Lasso regression are often perceived as more complex versions of linear regression. In reality, the prediction model remains exactly the same. What changes is the training objective. By adding a penalty on the coefficients, regularization forces the model to choose more stable solutions, especially when features are correlated. Implementing Ridge and Lasso step by step in Excel makes this idea explicit: regularization does not add complexity, it adds preference.

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]]> How to Increase Coding Iteration Speed https://towardsdatascience.com/how-to-increase-coding-iteration-speed/ Sat, 13 Dec 2025 13:30:00 +0000 https://towardsdatascience.com/?p=607895 Learn how to become a more efficient programmer with local testing

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]]> NeurIPS 2025 Best Paper Review: Qwen’s Systematic Exploration of Attention Gating https://towardsdatascience.com/neurips-2025-best-paper-review-qwens-systematic-exploration-of-attention-gating/ Sat, 13 Dec 2025 10:16:00 +0000 https://towardsdatascience.com/?p=607899 This one little trick can bring about enhanced training stability, the use of larger learning rates and improved scaling properties

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]]> The Machine Learning “Advent Calendar” Day 12: Logistic Regression in Excel https://towardsdatascience.com/the-machine-learning-advent-calendar-day-12-logistic-regression-in-excel/ Fri, 12 Dec 2025 17:15:00 +0000 https://towardsdatascience.com/?p=607901 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.

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]]> Decentralized Computation: The Hidden Principle Behind Deep Learning https://towardsdatascience.com/the-power-of-decentralization/ Fri, 12 Dec 2025 15:47:00 +0000 https://towardsdatascience.com/?p=607888 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 […]

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]]> The Machine Learning “Advent Calendar” Day 11: Linear Regression in Excel https://towardsdatascience.com/the-machine-learning-advent-calendar-day-11-linear-regression-in-excel/ Thu, 11 Dec 2025 16:31:00 +0000 https://towardsdatascience.com/?p=607891 Linear Regression looks simple, but it introduces the core ideas of modern machine learning: loss functions, optimization, gradients, scaling, and interpretation.
In this article, we rebuild Linear Regression in Excel, compare the closed-form solution with Gradient Descent, and see how the coefficients evolve step by step.
This foundation naturally leads to regularization, kernels, classification, and the dual view.
Linear Regression is not just a straight line, but the starting point for many models we will explore next in the Advent Calendar.

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]]> How Agent Handoffs Work in Multi-Agent Systems https://towardsdatascience.com/how-agent-handoffs-work-in-multi-agent-systems/ Thu, 11 Dec 2025 12:00:00 +0000 https://towardsdatascience.com/?p=607875 Understanding how LLM agents transfer control to each other in a multi-agent system with LangGraph

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]]> The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel https://towardsdatascience.com/the-machine-learning-advent-calendar-day-10-dbscan-in-excel/ Wed, 10 Dec 2025 16:30:00 +0000 https://towardsdatascience.com/?p=607882 DBSCAN shows how far we can go with a very simple idea: count how many neighbors live close to each point.
It finds clusters and marks anomalies without any probabilistic model, and it works beautifully in Excel.
But because it relies on one fixed radius, HDBSCAN is needed to make the method robust on real data.

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]]> How to Maximize Agentic Memory for Continual Learning https://towardsdatascience.com/how-to-maximize-agentic-memory-for-continual-learning/ Wed, 10 Dec 2025 15:00:00 +0000 https://towardsdatascience.com/?p=607873 Learn how to become an effective engineer with continual learning LLMs

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]]> Don’t Build an ML Portfolio Without These Projects https://towardsdatascience.com/dont-build-an-ml-portfolio-without-these-projects/ Wed, 10 Dec 2025 13:30:00 +0000 https://towardsdatascience.com/?p=607871 What recruiters are looking for in machine learning portfolios

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]]> Optimizing PyTorch Model Inference on AWS Graviton https://towardsdatascience.com/optimizing-pytorch-model-inference-on-aws-graviton/ Wed, 10 Dec 2025 12:00:00 +0000 https://towardsdatascience.com/?p=607814 Tips for accelerating AI/ML on CPU — Part 2

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]]> The Machine Learning “Advent Calendar” Day 9: LOF in Excel https://towardsdatascience.com/the-machine-learning-advent-calendar-day-9-lof-in-excel/ Tue, 09 Dec 2025 17:45:00 +0000 https://towardsdatascience.com/?p=607869 In this article, we explore LOF through three simple steps: distances and neighbors, reachability distances, and the final LOF score. Using tiny datasets, we see how two anomalies can look obvious to us but completely different to different algorithms. This reveals the key idea of unsupervised learning: there is no single “true” outlier, only definitions. Understanding these definitions is the real skill.

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]]> Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot https://towardsdatascience.com/personal-agentic-assistants-a-practical-blueprint-for-a-secure-multi-user-self-hosted-chatbot/ Tue, 09 Dec 2025 16:30:00 +0000 https://towardsdatascience.com/?p=607863 Build a self-hosted, end-to-end platform that gives each user a personal, agentic chatbot that can autonomously vector-search through files that the user explicitly allows it to access.

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]]> How to Develop AI-Powered Solutions, Accelerated by AI https://towardsdatascience.com/how-to-develop-ai-powered-solutions-accelerated-by-ai/ Tue, 09 Dec 2025 15:00:00 +0000 https://towardsdatascience.com/?p=607861 From idea to impact :  using AI as your accelerating copilot

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]]> GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems https://towardsdatascience.com/graphrag-in-practice-how-to-build-cost-efficient-high-recall-retrieval-systems/ Tue, 09 Dec 2025 13:30:00 +0000 https://towardsdatascience.com/?p=607859 Smarter retrieval strategies that outperform dense graphs — with hybrid pipelines and lower cost

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]]> A Realistic Roadmap to Start an AI Career in 2026 https://towardsdatascience.com/a-realistic-roadmap-to-start-an-ai-career-in-2026/ Tue, 09 Dec 2025 12:00:00 +0000 https://towardsdatascience.com/?p=607855 How to learn AI in 2026 through real, usable projects

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