Category: machine-learning
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The Machine Learning “Advent Calendar” Day 12: Logistic Regression in Excel
The Machine Learning “Advent Calendar” Day 12: Logistic Regression in Excel 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…
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Decentralized Computation: The Hidden Principle Behind Deep Learning
Decentralized Computation: The Hidden Principle Behind Deep Learning 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…
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The Machine Learning “Advent Calendar” Day 11: Linear Regression in Excel
The Machine Learning “Advent Calendar” Day 11: Linear Regression in Excel 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…
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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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Don’t Build an ML Portfolio Without These Projects
Don’t Build an ML Portfolio Without These Projects What recruiters are looking for in machine learning portfolios The post Don’t Build an ML Portfolio Without These Projects appeared first on Towards Data Science. Egor Howell Go to original source
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Optimizing PyTorch Model Inference on AWS Graviton
Optimizing PyTorch Model Inference on AWS Graviton Tips for accelerating AI/ML on CPU — Part 2 The post Optimizing PyTorch Model Inference on AWS Graviton appeared first on Towards Data Science. Chaim Rand Go to original source
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The Machine Learning “Advent Calendar” Day 9: LOF in Excel
The Machine Learning “Advent Calendar” Day 9: LOF in Excel 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…
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Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot
Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot 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. The post Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot appeared first…
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A Realistic Roadmap to Start an AI Career in 2026
A Realistic Roadmap to Start an AI Career in 2026 How to learn AI in 2026 through real, usable projects The post A Realistic Roadmap to Start an AI Career in 2026 appeared first on Towards Data Science. Sabrine Bendimerad Go to original source
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Bridging the Silence: How LEO Satellites and Edge AI Will Democratize Connectivity
Bridging the Silence: How LEO Satellites and Edge AI Will Democratize Connectivity Why on-device intelligence and low-orbit constellations are the only viable path to universal accessibility The post Bridging the Silence: How LEO Satellites and Edge AI Will Democratize Connectivity appeared first on Towards Data Science. Aakash Goswami Go to original source
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The Machine Learning “Advent Calendar” Day 8: Isolation Forest in Excel
The Machine Learning “Advent Calendar” Day 8: Isolation Forest in Excel Isolation Forest may look technical, but its idea is simple: isolate points using random splits. If a point is isolated quickly, it is an anomaly; if it takes many splits, it is normal. Using the tiny dataset 1, 2, 3, 9, we can see…
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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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Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained
Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained Understanding AI in 2026 — from machine learning to generative models The post Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained appeared first on Towards Data Science. Sabrine Bendimerad Go to original source
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Reading Research Papers in the Age of LLMs
Reading Research Papers in the Age of LLMs How I keep up with papers with a mix of manual and AI-assisted reading The post Reading Research Papers in the Age of LLMs appeared first on Towards Data Science. Parul Pandey Go to original source
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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 5: GMM in Excel
The Machine Learning “Advent Calendar” Day 5: GMM in Excel This article introduces the Gaussian Mixture Model as a natural extension of k-Means, by improving how distance is measured through variances and the Mahalanobis distance. Instead of assigning points to clusters with hard boundaries, GMM uses probabilities learned through the Expectation–Maximization algorithm – the general…
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On the Challenge of Converting TensorFlow Models to PyTorch
On the Challenge of Converting TensorFlow Models to PyTorch How to upgrade and optimize legacy AI/ML models The post On the Challenge of Converting TensorFlow Models to PyTorch appeared first on Towards Data Science. Chaim Rand Go to original source
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Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem
Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem A new NeurIPS 2025 paper shows how self-supervised learning imbues ViT with better image understanding than supervised learning The post Do Labels Make AI Blind? Self-Supervision Solves the Age-Old Binding Problem appeared first on Towards Data Science. Jonathan Williford Go to original source
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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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Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch
Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch PyTorch Model Performance Analysis and Optimization — Part 11 The post Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch appeared first on Towards Data Science. Chaim Rand 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 2: k-NN Classifier in Excel
The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel Exploring the k-NN classifier with its variants and improvements The post The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Machine Learning Lessons I’ve Learned This Month
The Machine Learning Lessons I’ve Learned This Month Christmas connections, Copilot’s costs, careful (no-)choices The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science. Pascal Janetzky 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 Problem with AI Browsers: Security Flaws and the End of Privacy
The Problem with AI Browsers: Security Flaws and the End of Privacy How Atlas and most current AI-powered browsers fail on three aspects: privacy, security, and censorship The post The Problem with AI Browsers: Security Flaws and the End of Privacy appeared first on Towards Data Science. Mike Huls Go to original source
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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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Metric Deception: When Your Best KPIs Hide Your Worst Failures
Metric Deception: When Your Best KPIs Hide Your Worst Failures The most dangerous KPIs aren’t broken; they’re the ones trusted long after they’ve lost their meaning. The post Metric Deception: When Your Best KPIs Hide Your Worst Failures appeared first on Towards Data Science. Shafeeq Ur Rahaman Go to original source
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Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them.
Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them. Neural and symbolic models compress the world in fundamentally different ways, and Sparse Autoencoders (SAEs) offer a bridge to connect them. The post Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them. appeared first on Towards…
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RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture Radar
RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture Radar The high-resolution physics turning microwave echoes into real-time flood intelligence The post RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture Radar appeared first on Towards Data Science. Aakash Goswami Go to original source
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Ten Lessons of Building LLM Applications for Engineers
Ten Lessons of Building LLM Applications for Engineers Practical field notes on workflows, structure, and evaluation from two years of building with engineering domain experts. The post Ten Lessons of Building LLM Applications for Engineers appeared first on Towards Data Science. Shuai Guo Go to original source
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Learning Triton One Kernel at a Time: Softmax
Learning Triton One Kernel at a Time: Softmax All you need to know about a fast, readable and PyTorch-ready softmax kernel The post Learning Triton One Kernel at a Time: Softmax appeared first on Towards Data Science. Ryan Pégoud Go to original source
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Empirical Mode Decomposition: The Most Intuitive Way to Decompose Complex Signals and Time Series
Empirical Mode Decomposition: The Most Intuitive Way to Decompose Complex Signals and Time Series A step-by-step breakdown of empirical mode decomposition to help you extract patterns from time series The post Empirical Mode Decomposition: The Most Intuitive Way to Decompose Complex Signals and Time Series appeared first on Towards Data Science. Sabrine Bendimerad Go to…
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Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off
Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off The best models live in the sweet spot: generalizing well, learning enough, but not too much The post Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off appeared first on Towards Data Science. Frida Karvouni Go to original source
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Generative AI Will Redesign Cars, But Not the Way Automakers Think
Generative AI Will Redesign Cars, But Not the Way Automakers Think Traditional manufacturers are using revolutionary technology for incremental optimization instead of fundamental re-imagination The post Generative AI Will Redesign Cars, But Not the Way Automakers Think appeared first on Towards Data Science. Nishant Arora Go to original source
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How to Use Gemini 3 Pro Efficiently
How to Use Gemini 3 Pro Efficiently Learn the pros and cons of Gemini 3 Pro, from testing with both coding and console usage The post How to Use Gemini 3 Pro Efficiently appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How Relevance Models Foreshadowed Transformers for NLP
How Relevance Models Foreshadowed Transformers for NLP Tracing the history of LLM attention: standing on the shoulders of giants The post How Relevance Models Foreshadowed Transformers for NLP appeared first on Towards Data Science. Sean Moran Go to original source
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Why I’m Making the Switch to marimo Notebooks
Why I’m Making the Switch to marimo Notebooks A fresh way to think about computational notebooks The post Why I’m Making the Switch to marimo Notebooks appeared first on Towards Data Science. Parul Pandey Go to original source
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PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch
PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch Hands-on PyTorch: Building a 3-layer neural network for multiple regression The post PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch appeared first on Towards Data Science. Gustavo Santos Go to original source
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How to Build an Over-Engineered Retrieval System
How to Build an Over-Engineered Retrieval System Which is actually how some people do it The post How to Build an Over-Engineered Retrieval System appeared first on Towards Data Science. Ida Silfverskiöld Go to original source
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Understanding Convolutional Neural Networks (CNNs) Through Excel
Understanding Convolutional Neural Networks (CNNs) Through Excel Deep learning is often seen as a black box. We know that it learns from data, but we rarely stop to ask how it truly learns. What if we could open that box and watch each step happen right before our eyes? With Excel, we can do exactly…
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Introducing ShaTS: A Shapley-Based Method for Time-Series Models
Introducing ShaTS: A Shapley-Based Method for Time-Series Models Why you should not explain your time-series data with tabular Shapley methods The post Introducing ShaTS: A Shapley-Based Method for Time-Series Models appeared first on Towards Data Science. Manuel Franco de la Peña Go to original source
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I Measured Neural Network Training Every 5 Steps for 10,000 Iterations
I Measured Neural Network Training Every 5 Steps for 10,000 Iterations Image by Pixabay.com The post I Measured Neural Network Training Every 5 Steps for 10,000 Iterations appeared first on Towards Data Science. Javier Marin Go to original source
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How to Crack Machine Learning System-Design Interviews
How to Crack Machine Learning System-Design Interviews A comprehensive guide into Meta, Apple, Reddit, Amazon, Google, and Snap ML design interviews The post How to Crack Machine Learning System-Design Interviews appeared first on Towards Data Science. Aliaksei Mikhailiuk Go to original source
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Music, Lyrics, and Agentic AI: Building a Smart Song Explainer using Python and OpenAI
Music, Lyrics, and Agentic AI: Building a Smart Song Explainer using Python and OpenAI This is how to build an AI-powered Song Explainer using Python and OpenAI The post Music, Lyrics, and Agentic AI: Building a Smart Song Explainer using Python and OpenAI appeared first on Towards Data Science. Piero Paialunga Go to original source
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Critical Mistakes Companies Make When Integrating AI/ML into Their Processes
Critical Mistakes Companies Make When Integrating AI/ML into Their Processes What I’ve learned leading AI teams across industries The post Critical Mistakes Companies Make When Integrating AI/ML into Their Processes appeared first on Towards Data Science. Andrey Chubin Go to original source
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Organizing Code, Experiments, and Research for Kaggle Competitions
Organizing Code, Experiments, and Research for Kaggle Competitions Lessons and tips learned while earning a Kaggle Competition Medal The post Organizing Code, Experiments, and Research for Kaggle Competitions appeared first on Towards Data Science. Ibrahim Habib Go to original source
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Spearman Correlation Coefficient for When Pearson Isn’t Enough
Spearman Correlation Coefficient for When Pearson Isn’t Enough Not all relationships are linear, and that is where Spearman comes in. The post Spearman Correlation Coefficient for When Pearson Isn’t Enough appeared first on Towards Data Science. Nikhil Dasari Go to original source
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The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning, or an LLM (Explained with One Example)
The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning, or an LLM (Explained with One Example) A practical use case to describe how the data scientist job changed across three generations of machine learning The post The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning,…
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Why Storytelling With Data Matters for Business and Data Analysts
Why Storytelling With Data Matters for Business and Data Analysts Data is driving the future of business and here’s how you can be prepared for that future The post Why Storytelling With Data Matters for Business and Data Analysts appeared first on Towards Data Science. Rashi Desai Go to original source
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Does More Data Always Yield Better Performance?
Does More Data Always Yield Better Performance? Exploring and challenging the conventional wisdom of “more data → better performance” by experimenting with the interactions between sample size, attribute set, and model complexity. The post Does More Data Always Yield Better Performance? appeared first on Towards Data Science. Mohannad Elhamod Go to original source
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Evaluating Synthetic Data — The Million Dollar Question
Evaluating Synthetic Data — The Million Dollar Question Learn how to evaluate synthetic data quality using the Maximum Similarity Test — a simple, quantitative approach for assessing fidelity, utility, and privacy in synthetic datasets. The post Evaluating Synthetic Data — The Million Dollar Question appeared first on Towards Data Science. Andrew Skabar Go to original…
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How to Use GPT-5 Effectively
How to Use GPT-5 Effectively Learn about GPT-5’s features and settings, and how to optimally apply them to your use case The post How to Use GPT-5 Effectively appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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The Reinforcement Learning Handbook: A Guide to Foundational Questions
The Reinforcement Learning Handbook: A Guide to Foundational Questions Simplifying all the concepts required to master reinforcement learning The post The Reinforcement Learning Handbook: A Guide to Foundational Questions appeared first on Towards Data Science. Avishek Biswas Go to original source
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We Didn’t Invent Attention — We Just Rediscovered It
We Didn’t Invent Attention — We Just Rediscovered It How selective amplification emerged across evolution, chemistry, and AI through convergent mathematical solutions The post We Didn’t Invent Attention — We Just Rediscovered It appeared first on Towards Data Science. Javier Marin Go to original source
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Why Nonparametric Models Deserve a Second Look
Why Nonparametric Models Deserve a Second Look Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms. The post Why Nonparametric Models Deserve a Second Look appeared first on Towards Data Science. Andrew Skabar Go to original source
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What to Do When Your Credit Risk Model Works Today, but Breaks Six Months Later
What to Do When Your Credit Risk Model Works Today, but Breaks Six Months Later Here’s why it happens — and how to fix it The post What to Do When Your Credit Risk Model Works Today, but Breaks Six Months Later appeared first on Towards Data Science. Javier Marin Go to original source
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It Doesn’t Need to Be a Chatbot
It Doesn’t Need to Be a Chatbot A more organic, incremental approach to integrating AI into existing products The post It Doesn’t Need to Be a Chatbot appeared first on Towards Data Science. Dr. Janna Lipenkova Go to original source
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From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers
From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers From ARIMA to N-BEATS: Comparing forecasting approaches that balance accuracy, interpretability, and sustainability The post From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers appeared first on Towards Data Science. Dr. Theophano Mitsa Go…
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MobileNetV3 Paper Walkthrough: The Tiny Giant Getting Even Smarter
MobileNetV3 Paper Walkthrough: The Tiny Giant Getting Even Smarter MobileNetV3 with PyTorch — now featuring SE blocks and hard activation functions The post MobileNetV3 Paper Walkthrough: The Tiny Giant Getting Even Smarter appeared first on Towards Data Science. Muhammad Ardi Go to original source
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The Pearson Correlation Coefficient, Explained Simply
The Pearson Correlation Coefficient, Explained Simply A simple explanation of the Pearson correlation coefficient with examples The post The Pearson Correlation Coefficient, Explained Simply appeared first on Towards Data Science. Nikhil Dasari Go to original source
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The Machine Learning Projects Employers Want to See
The Machine Learning Projects Employers Want to See What machine learning projects will actually get you interviews and jobs The post The Machine Learning Projects Employers Want to See appeared first on Towards Data Science. Egor Howell Go to original source
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Deep Reinforcement Learning: 0 to 100
Deep Reinforcement Learning: 0 to 100 Using RL to teach robots to fly a drone The post Deep Reinforcement Learning: 0 to 100 appeared first on Towards Data Science. Vedant Jumle Go to original source
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How to Apply Powerful AI Audio Models to Real-World Applications
How to Apply Powerful AI Audio Models to Real-World Applications Learn about different types of AI audio models and the application areas they can be used in. The post How to Apply Powerful AI Audio Models to Real-World Applications appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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The Machine Learning Lessons I’ve Learned This Month
The Machine Learning Lessons I’ve Learned This Month October 2025: READMEs, MIGs, and movements The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science. Pascal Janetzky Go to original source
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Agentic AI from First Principles: Reflection
Agentic AI from First Principles: Reflection From theory to code: building feedback loops that improve LLM accuracy The post Agentic AI from First Principles: Reflection appeared first on Towards Data Science. Mariya Mansurova Go to original source
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Choosing the Best Model Size and Dataset Size under a Fixed Budget for LLMs
Choosing the Best Model Size and Dataset Size under a Fixed Budget for LLMs A small-scale exploration using Tiny Transformers The post Choosing the Best Model Size and Dataset Size under a Fixed Budget for LLMs appeared first on Towards Data Science. Shuyang Go to original source
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When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation
When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation Exploring the frequency fingerprints of Transformers to guide smarter knowledge distillation The post When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation appeared first on Towards Data Science. Ankit Singh Chauhan Go to original source
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Multiple Linear Regression Explained Simply (Part 1)
Multiple Linear Regression Explained Simply (Part 1) The math behind fitting a plane instead of a line. The post Multiple Linear Regression Explained Simply (Part 1) appeared first on Towards Data Science. Nikhil Dasari Go to original source
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Federated Learning and Custom Aggregation Schemes
Federated Learning and Custom Aggregation Schemes A practical guide to designing and analyzing robust aggregation strategies The post Federated Learning and Custom Aggregation Schemes appeared first on Towards Data Science. Salman Toor Go to original source
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Scaling Recommender Transformers to a Billion Parameters
Scaling Recommender Transformers to a Billion Parameters How to implement a new generation of transformer recommenders The post Scaling Recommender Transformers to a Billion Parameters appeared first on Towards Data Science. Kirill Кhrylchenko Go to original source
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How to Build An AI Agent with Function Calling and GPT-5
How to Build An AI Agent with Function Calling and GPT-5 How an AI agent works: a step-by-step guide The post How to Build An AI Agent with Function Calling and GPT-5 appeared first on Towards Data Science. Ayoola Olafenwa Go to original source
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How to Use Frontier Vision LLMs: Qwen3-VL
How to Use Frontier Vision LLMs: Qwen3-VL Learn how to apply VLMs to advanced document understanding tasks The post How to Use Frontier Vision LLMs: Qwen3-VL appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How I Tailored the Resume That Landed Me $100K+ Data Science and ML Offers
How I Tailored the Resume That Landed Me $100K+ Data Science and ML Offers How to write a data science and machine learning resume that actually lands jobs. The post How I Tailored the Resume That Landed Me $100K+ Data Science and ML Offers appeared first on Towards Data Science. Egor Howell Go to original…
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Things I Learned by Participating in GenAI Hackathons Over the Past 6 Months
Things I Learned by Participating in GenAI Hackathons Over the Past 6 Months Sharing my two cents from the building in public journey so far The post Things I Learned by Participating in GenAI Hackathons Over the Past 6 Months appeared first on Towards Data Science. Parul Pandey 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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How to Classify Lung Cancer Subtype from DNA Copy Numbers Using PyTorch
How to Classify Lung Cancer Subtype from DNA Copy Numbers Using PyTorch A step-by-step introduction to understanding cancer from the perspective of a data scientist. The post How to Classify Lung Cancer Subtype from DNA Copy Numbers Using PyTorch appeared first on Towards Data Science. Adam Streck Go to original source
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How I Used Machine Learning to Predict 41% of Project Delays Before They Happened
How I Used Machine Learning to Predict 41% of Project Delays Before They Happened How data science can help project managers anticipate risks and save time The post How I Used Machine Learning to Predict 41% of Project Delays Before They Happened appeared first on Towards Data Science. Yassin Zehar Go to original source
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Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator
Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator Applying calculus fundamentals to computer vision for edge detection The post Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator appeared first on Towards Data Science. Vyacheslav Efimov Go to original source
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Stop Feeling Lost : How to Master ML System Design
Stop Feeling Lost : How to Master ML System Design What machine learning system design is and how to prepare for it The post Stop Feeling Lost : How to Master ML System Design appeared first on Towards Data Science. Egor Howell Go to original source
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How to Build Tools for AI Agents
How to Build Tools for AI Agents Learn how to design and build effective tools to be used by AI Agents The post How to Build Tools for AI Agents appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Learning Triton One Kernel at a Time: Matrix Multiplication
Learning Triton One Kernel at a Time: Matrix Multiplication Tiled GEMM, GPU memory, coalescing, and much more! The post Learning Triton One Kernel at a Time: Matrix Multiplication appeared first on Towards Data Science. Ryan Pégoud Go to original source
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How the Rise of Tabular Foundation Models Is Reshaping Data Science
How the Rise of Tabular Foundation Models Is Reshaping Data Science A turning point for data analysis? The post How the Rise of Tabular Foundation Models Is Reshaping Data Science appeared first on Towards Data Science. Pirmin Lemberger Go to original source
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How to Perform Effective Agentic Context Engineering
How to Perform Effective Agentic Context Engineering Learn how to optimize the context of your agents, for powerful agentic performance The post How to Perform Effective Agentic Context Engineering appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Building a Command-Line Quiz Application in R
Building a Command-Line Quiz Application in R Practice control flow, input handling, and functions in R by creating an interactive quiz game. The post Building a Command-Line Quiz Application in R appeared first on Towards Data Science. Benjamin Nweke Go to original source
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How to Build a Powerful Deep Research System
How to Build a Powerful Deep Research System Learn how to access vasts amounts of information with your own deep research system The post How to Build a Powerful Deep Research System appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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MobileNetV2 Paper Walkthrough: The Smarter Tiny Giant
MobileNetV2 Paper Walkthrough: The Smarter Tiny Giant Understanding and implementing MobileNetV2 with PyTorch — the next generation of MobileNetV1 The post MobileNetV2 Paper Walkthrough: The Smarter Tiny Giant appeared first on Towards Data Science. Muhammad Ardi Go to original source
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What Makes a Language Look Like Itself?
What Makes a Language Look Like Itself? How simple statistics reveal the visual fingerprints of 20 languages The post What Makes a Language Look Like Itself? appeared first on Towards Data Science. Kenneth McCarthy Go to original source
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Smarter, Not Harder: How AI’s Self-Doubt Unlocks Peak Performance
Smarter, Not Harder: How AI’s Self-Doubt Unlocks Peak Performance “Deep Think with Confidence,” a smarter way to scale reasoning tasks without wasting a massive amount of computation The post Smarter, Not Harder: How AI’s Self-Doubt Unlocks Peak Performance appeared first on Towards Data Science. Ankit Singh Chauhan Go to original source
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Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide
Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide Comparing model-free and model-based RL methods on a dynamic grid world The post Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide appeared first on Towards Data Science. Ryan Pégoud Go to original source
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How to Improve the Efficiency of Your PyTorch Training Loop
How to Improve the Efficiency of Your PyTorch Training Loop Learn how to diagnose and resolve bottlenecks in PyTorch using the num_workers, pin_memory, and profiler parameters to maximize training performance. The post How to Improve the Efficiency of Your PyTorch Training Loop appeared first on Towards Data Science. Andrea D’Agostino Go to original source
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Visual Pollen Classification Using CNNs and Vision Transformers
Visual Pollen Classification Using CNNs and Vision Transformers Filling the data gap: A machine learning approach to pollen identification in ecology and biotechnology The post Visual Pollen Classification Using CNNs and Vision Transformers appeared first on Towards Data Science. Karol Struniawski Go to original source
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Beyond ROC-AUC and KS: The Gini Coefficient, Explained Simply
Beyond ROC-AUC and KS: The Gini Coefficient, Explained Simply Understanding Gini and Lorenz curves for smarter model evaluation The post Beyond ROC-AUC and KS: The Gini Coefficient, Explained Simply appeared first on Towards Data Science. Nikhil Dasari Go to original source