Category: Algorithms
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The Machine Learning “Advent Calendar” Day 21: Gradient Boosted Decision Tree Regressor in Excel
The Machine Learning “Advent Calendar” Day 21: Gradient Boosted Decision Tree Regressor in Excel Gradient descent in function space with decision trees The post The Machine Learning “Advent Calendar” Day 21: Gradient Boosted Decision Tree Regressor in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning “Advent Calendar” Day 18: Neural Network Classifier in Excel
The Machine Learning “Advent Calendar” Day 18: Neural Network Classifier in Excel Understanding forward propagation and backpropagation through explicit formulas The post The Machine Learning “Advent Calendar” Day 18: Neural Network Classifier in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs
The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs An optimal solution to the well-known NP-complete problem, when the input values are close enough to each other. The post The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs appeared first on Towards Data Science. Tigran Hayrapetyan Go to original…
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The Machine Learning “Advent Calendar” Day 16: Kernel Trick in Excel
The Machine Learning “Advent Calendar” Day 16: Kernel Trick in Excel Kernel SVM often feels abstract, with kernels, dual formulations, and support vectors. In this article, we take a different path. Starting from Kernel Density Estimation, we build Kernel SVM step by step as a sum of local bells, weighted and selected by hinge loss,…
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The Machine Learning “Advent Calendar” Day 15: SVM in Excel
The Machine Learning “Advent Calendar” Day 15: SVM in Excel 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…
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The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel
The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel 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,…
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The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier
The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier In Day 6, we saw how a Decision Tree Regressor finds its optimal split by minimizing the Mean Squared Error. Today, for Day 7 of the Machine Learning “Advent Calendar”, we switch to classification. With just one numerical feature and two classes, we explore how…
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The Machine Learning “Advent Calendar” Day 6: Decision Tree Regressor
The Machine Learning “Advent Calendar” Day 6: Decision Tree Regressor During the first days of this Machine Learning Advent Calendar, we explored models based on distances. Today, we switch to a completely different way of learning: Decision Trees. With a simple one-feature dataset, we can see how a tree chooses its first split. The idea…
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The Machine Learning “Advent Calendar” Day 4: k-Means in Excel
The Machine Learning “Advent Calendar” Day 4: k-Means in Excel How to implement a training algorithm that finally looks like “real” machine learning The post The Machine Learning “Advent Calendar” Day 4: k-Means in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel
The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel From local distance to global probability The post The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning “Advent Calendar” Day 1: k-NN Regressor in Excel
The Machine Learning “Advent Calendar” Day 1: k-NN Regressor in Excel This first day of the Advent Calendar introduces the k-NN regressor, the simplest distance-based model. Using Excel, we explore how predictions rely entirely on the closest observations, why feature scaling matters, and how heterogeneous variables can make distances meaningless. Through examples with continuous and…
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The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint
The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint Opening the black box of ML models, step by step, directly in Excel The post The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint appeared first on Towards Data Science. angela shi Go to original source
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The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall
The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall A modification to the Boruta algorithm that dramatically reduces computation while maintaining high sensitivity The post The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall appeared first on Towards Data Science. Nicolas Vana Go to original source
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Building a Rules Engine from First Principles
Building a Rules Engine from First Principles How recasting propositional logic as sparse algebra leads to an elegant and efficient design The post Building a Rules Engine from First Principles appeared first on Towards Data Science. Dmitry Lesnik Go to original source
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Implementing the Fourier Transform Numerically in Python: A Step-by-Step Guide
Implementing the Fourier Transform Numerically in Python: A Step-by-Step Guide What if the FFT functions in NumPy and SciPy don’t actually compute the Fourier transform you think they do? The post Implementing the Fourier Transform Numerically in Python: A Step-by-Step Guide appeared first on Towards Data Science. JUNIOR JUMBONG Go to original source
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Machine Learning Meets Panel Data: What Practitioners Need to Know
Machine Learning Meets Panel Data: What Practitioners Need to Know How to avoid overestimating machine learning models’ performance, usefulness, and real-world applicability due to hidden data leakage The post Machine Learning Meets Panel Data: What Practitioners Need to Know appeared first on Towards Data Science. Marco Letta Go to original source
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From Genes to Neural Networks: Understanding and Building NEAT (Neuro-Evolution of Augmenting Topologies) from Scratch
From Genes to Neural Networks: Understanding and Building NEAT (Neuro-Evolution of Augmenting Topologies) from Scratch Practical Neuroevolution: Reproducing NEAT’s Innovations and Code Walkthrough The post From Genes to Neural Networks: Understanding and Building NEAT (Neuro-Evolution of Augmenting Topologies) from Scratch appeared first on Towards Data Science. Carlos Redondo Go to original source
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The Five-Second Fingerprint: Inside Shazam’s Instant Song ID
The Five-Second Fingerprint: Inside Shazam’s Instant Song ID How Shazam recognizes songs in seconds The post The Five-Second Fingerprint: Inside Shazam’s Instant Song ID appeared first on Towards Data Science. Ashton Gribble Go to original source
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A Gentle Introduction to Backtracking
A Gentle Introduction to Backtracking Conceptual overview and hands-on examples The post A Gentle Introduction to Backtracking appeared first on Towards Data Science. Chinmay Kakatkar Go to original source
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Algorithm Protection in the Context of Federated Learning
Algorithm Protection in the Context of Federated Learning While working at a biotech company, we aim to advance ML & AI Algorithms to enable, for example, brain lesion segmentation to be executed at the hospital/clinic location where patient data resides, so it is processed in a secure manner. This, in essence, is guaranteed by federated…