PinnedInLow Code for Data SciencebyJamie Crossman-Smith·May 25, 2024The AI Regulation Dilemma: Ethical Progress or Dystopian Future?Will we use AI responsibly to solve our biggest problems, or let it run wild and cause chaos?
Jamie Crossman-Smith·May 21, 2025Innovation Concepts and Techniques for Digital Transformation in Mid-Market FirmsIt’s never about the tech. It’s always about your mindset and culture.
InLow Code for Data SciencebyJamie Crossman-Smith·Apr 5, 2025Large Language Models: Our Best Working Model of the Human Mind?What will AI teach us about us?
InLow Code for Data SciencebyJamie Crossman-Smith·Mar 2, 2025Distance Metrics in Machine LearningEuclidean, Manhattan, Minkowski, Cosine, Levenshtein and more — A walkthrough of the most widely used ones
InLow Code for Data SciencebyJamie Crossman-Smith·Feb 9, 2025The Paradox of Naive Bayes: When Simple Becomes SophisticatedA theoretical intro that shows how its theoretical elegance, practical efficiency and effectiveness continue to make it a valuable tool…A response icon1A response icon1
InLow Code for Data SciencebyJamie Crossman-Smith·Feb 3, 2025The Complete Guide to Support Vector Machines: From Theory to PracticeHyperparameters, advantages, limitations, real-world applications and more
Jamie Crossman-Smith·Nov 7, 2024Machine Learning for Auditors and Finance Teams: A Simple GuideWhy Machine Learning?
Jamie Crossman-Smith·Oct 15, 2024Reducing Bias in AI: How Compliance and Internal Audit Specialists Can Leverage FairlearnPhoto by Google DeepMind on Unsplash
Jamie Crossman-Smith·Jun 5, 2024The Future of Internal Audit in the Age of AI: It’s Not About the Technology at All.This is behind a paywall. If you would like to read the article please click here.
InLow Code for Data SciencebyJamie Crossman-Smith·Apr 22, 2024Embracing Digital Transformation: A Survival Guide for Mid-Market FirmsSuccess does not lie just in the adoption of new technologies, but in the ability to adapt, learn, and continuously improve
