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A Simple Implementation of the Attention Mechanism from Scratch

Introduction The Attention Mechanism is often associated with the transformer architecture, but it was already used in RNNs. In Machine Translation or MT (e.g., English-Italian) tasks, when you want to predict the next Italian word, you need your model to focus, or pay attention, on the most important English words that are useful to make […]

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Create Your Supply Chain Analytics Portfolio to Land Your Dream Job

Supply chains are under pressure like never before. From climate-driven disruptions to geopolitical shifts, businesses must adapt to rising costs, new trade barriers and growing sustainability demands. In this new world where supply chains face uncertainty, Supply Chain Analytics is essential to keep resilient operations. Samir, can you advise me on how to build a supply chain analytics

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A Little More Conversation, A Little Less Action — A Case Against Premature Data Integration

When I talk to [large] organisations that have not yet properly started with Data Science (DS) and Machine Learning (ML), they often tell me that they have to run a data integration project first, because “…all the data is scattered across the organisation, hidden in silos and packed away at odd formats on obscure servers

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Master the 3D Reconstruction Process: A Step-by-Step Guide

The 3d Reconstruction journey from 2D photographs to 3D models follows a structured path.  This path consists of distinct steps that build upon each other to transform flat images into spatial information.  Understanding this pipeline is crucial for anyone looking to create high-quality 3D reconstructions. Let me explain… Most people think 3D reconstruction means: Taking

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From Physics to Probability: Hamiltonian Mechanics for Generative Modeling and MCMC

Phase space of a nonlinear pendulum. Photo by the author. Hamiltonian mechanics is a way to describe how physical systems, like planets or pendulums, move over time, focusing on energy rather than just forces. By reframing complex dynamics through energy lenses, this 19th-century physics framework now powers cutting-edge generative AI. It uses generalized coordinates (

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Data Science: From School to Work, Part III

Introduction Writing code is about solving problems, but not every problem is predictable. In the real world, your software will encounter unexpected situations: missing files, invalid user inputs, network timeouts, or even hardware failures. This is why handling errors isn’t just a nice-to-have; it’s a critical part of building robust and reliable applications for production.

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