Category: deep-dives
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How Human Work Will Remain Valuable in an AI World
How Human Work Will Remain Valuable in an AI World The Road to Reality — Episode 1 The post How Human Work Will Remain Valuable in an AI World appeared first on Towards Data Science. Favio Vázquez Go to original source
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Agentic RAG vs Classic RAG: From a Pipeline to a Control Loop
Agentic RAG vs Classic RAG: From a Pipeline to a Control Loop A practical guide to choosing between single-pass pipelines and adaptive retrieval loops based on your use case’s complexity, cost, and reliability requirements The post Agentic RAG vs Classic RAG: From a Pipeline to a Control Loop appeared first on Towards Data Science. Mostafa…
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YOLOv3 Paper Walkthrough: Even Better, But Not That Much
YOLOv3 Paper Walkthrough: Even Better, But Not That Much A PyTorch implementation on the YOLOv3 architecture from scratch The post YOLOv3 Paper Walkthrough: Even Better, But Not That Much appeared first on Towards Data Science. Muhammad Ardi Go to original source
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Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale
Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale Reducing LLM costs by 30% with validation-aware, multi-tier caching The post Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale appeared first on Towards Data Science. Partha Sarkar Go to original source
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Context Engineering as Your Competitive Edge
Context Engineering as Your Competitive Edge If you have both unique domain expertise and know how to make it usable to your AI systems, you’ll be hard to beat. The post Context Engineering as Your Competitive Edge appeared first on Towards Data Science. Dr. Janna Lipenkova Go to original source
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Detecting and Editing Visual Objects with Gemini
Detecting and Editing Visual Objects with Gemini A practical guide to identifying, restoring, and transforming elements within your images The post Detecting and Editing Visual Objects with Gemini appeared first on Towards Data Science. Laurent Picard Go to original source
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Take a Deep Dive into Filtering in DAX
Take a Deep Dive into Filtering in DAX Have you ever wondered what happens when you apply a filter in a DAX expression? Well, Today I will take you on a deep dive into this fascinating topic, with examples to help you learn something new and surprising. The post Take a Deep Dive into Filtering…
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Scaling Feature Engineering Pipelines with Feast and Ray
Scaling Feature Engineering Pipelines with Feast and Ray Utilizing feature stores like Feast and distributed compute frameworks like Ray in production machine learning systems The post Scaling Feature Engineering Pipelines with Feast and Ray appeared first on Towards Data Science. Kenneth Leung Go to original source
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Aliasing in Audio, Easily Explained: From Wagon Wheels to Waveforms
Aliasing in Audio, Easily Explained: From Wagon Wheels to Waveforms Understanding the foundational distortion of digital audio from first principles, with worked examples and visual intuition The post Aliasing in Audio, Easily Explained: From Wagon Wheels to Waveforms appeared first on Towards Data Science. Aman Agrawal Go to original source
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Optimizing Token Generation in PyTorch Decoder Models
Optimizing Token Generation in PyTorch Decoder Models Hiding host-device synchronization via CUDA stream interleaving The post Optimizing Token Generation in PyTorch Decoder Models appeared first on Towards Data Science. Chaim Rand Go to original source
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Optimizing Deep Learning Models with SAM
Optimizing Deep Learning Models with SAM A deep dive into the Sharpness-Aware-Minimization (SAM) algorithm and how it improves the generalizability of modern deep learning models The post Optimizing Deep Learning Models with SAM appeared first on Towards Data Science. Anindya Dey Go to original source
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PySpark for Pandas Users
PySpark for Pandas Users Common Pandas operations and their equivalents in PySpark The post PySpark for Pandas Users appeared first on Towards Data Science. Thomas Reid Go to original source
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An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI
An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI The guide to automated improvement of scientific and industrial repositories using open-source AI agents The post An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI appeared first on Towards Data Science. Nikolay Nikitin Go to original source
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From Monolith to Contract-Driven Data Mesh
From Monolith to Contract-Driven Data Mesh A pragmatic journey using website analytics as a real-world example The post From Monolith to Contract-Driven Data Mesh appeared first on Towards Data Science. Corné POTGIETER Go to original source
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Understanding the Chi-Square Test Beyond the Formula
Understanding the Chi-Square Test Beyond the Formula How categorical data becomes statistical evidence. The post Understanding the Chi-Square Test Beyond the Formula appeared first on Towards Data Science. Nikhil Dasari Go to original source
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Can AI Solve Failures in Your Supply Chain?
Can AI Solve Failures in Your Supply Chain? When your warehouse and transportation teams blame each other for late deliveries, who’s right? We can ask an agent connected to the data settle the debate. The post Can AI Solve Failures in Your Supply Chain? appeared first on Towards Data Science. Samir Saci Go to original…
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Agentic AI for Modern Deep Learning Experimentation
Agentic AI for Modern Deep Learning Experimentation Stop babysitting training runs. Start shipping research. Autonomous experiment management built for/by deep learning engineers. The post Agentic AI for Modern Deep Learning Experimentation appeared first on Towards Data Science. Sam Black Go to original source
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The Strangest Bottleneck in Modern LLMs
The Strangest Bottleneck in Modern LLMs Why insanely fast GPUs still can’t make LLMs feel instant The post The Strangest Bottleneck in Modern LLMs appeared first on Towards Data Science. Moulik Gupta Go to original source
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AI in Multiple GPUs: Point-to-Point and Collective Operations
AI in Multiple GPUs: Point-to-Point and Collective Operations Learn PyTorch distributed operations for multi GPU AI workloads The post AI in Multiple GPUs: Point-to-Point and Collective Operations appeared first on Towards Data Science. Lorenzo Cesconetto Go to original source
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The Death of the “Everything Prompt”: Google’s Move Toward Structured AI
The Death of the “Everything Prompt”: Google’s Move Toward Structured AI How the new Interactions API enables deep-reasoning, stateful, agentic workflows. The post The Death of the “Everything Prompt”: Google’s Move Toward Structured AI appeared first on Towards Data Science. Thomas Reid Go to original source
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Mechanistic Interpretability: Peeking Inside an LLM
Mechanistic Interpretability: Peeking Inside an LLM Are the human-like cognitive abilities of LLMs real or fake? How does information travel through the neural network? Is there hidden knowledge inside an LLM? The post Mechanistic Interpretability: Peeking Inside an LLM appeared first on Towards Data Science. Julian Mendel Go to original source
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Why Is My Code So Slow? A Guide to Py-Spy Python Profiling
Why Is My Code So Slow? A Guide to Py-Spy Python Profiling Stop guessing and start diagnosing performance issues using Py-Spy The post Why Is My Code So Slow? A Guide to Py-Spy Python Profiling appeared first on Towards Data Science. Kenneth McCarthy Go to original source
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How to Build Your Own Custom LLM Memory Layer from Scratch
How to Build Your Own Custom LLM Memory Layer from Scratch Step-by-step guide to building autonomous memory retrieval systems The post How to Build Your Own Custom LLM Memory Layer from Scratch appeared first on Towards Data Science. Avishek Biswas Go to original source
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Plan–Code–Execute: Designing Agents That Create Their Own Tools
Plan–Code–Execute: Designing Agents That Create Their Own Tools The case against pre-built tools in Agentic Architectures The post Plan–Code–Execute: Designing Agents That Create Their Own Tools appeared first on Towards Data Science. Partha Sarkar Go to original source
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Routing in a Sparse Graph: a Distributed Q-Learning Approach
Routing in a Sparse Graph: a Distributed Q-Learning Approach Distributed agents need only decide one move ahead. The post Routing in a Sparse Graph: a Distributed Q-Learning Approach appeared first on Towards Data Science. Sébastien Gilbert Go to original source
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Creating a Data Pipeline to Monitor Local Crime Trends
Creating a Data Pipeline to Monitor Local Crime Trends A walkthough of creating an ETL pipeline to extract local crime data and visualize it in Metabase. The post Creating a Data Pipeline to Monitor Local Crime Trends appeared first on Towards Data Science. Jimin Kang Go to original source
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Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents”
Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents” Hard-won lessons on how to scale agentic systems without scaling the chaos, including a taxonomy of core agent types. The post Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents” appeared first…
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On the Possibility of Small Networks for Physics-Informed Learning
On the Possibility of Small Networks for Physics-Informed Learning A new kind of hyperparameter study The post On the Possibility of Small Networks for Physics-Informed Learning appeared first on Towards Data Science. Conor Rowan Go to original source
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Federated Learning, Part 2: Implementation with the Flower Framework 🌼
Federated Learning, Part 2: Implementation with the Flower Framework 🌼 Implementing cross-silo federated learning step by step The post Federated Learning, Part 2: Implementation with the Flower Framework 🌼 appeared first on Towards Data Science. Parul Pandey Go to original source
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Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning
Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning Estimating neighborhood-level pedestrian risk from real-world incident data The post Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning appeared first on Towards Data Science. Aneesh Patil Go to original source
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Going Beyond the Context Window: Recursive Language Models in Action
Going Beyond the Context Window: Recursive Language Models in Action Explore a practical approach to analysing massive datasets with LLMs The post Going Beyond the Context Window: Recursive Language Models in Action appeared first on Towards Data Science. Mariya Mansurova Go to original source
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From Connections to Meaning: Why Heterogeneous Graph Transformers (HGT) Change Demand Forecasting
From Connections to Meaning: Why Heterogeneous Graph Transformers (HGT) Change Demand Forecasting How relationship-aware graphs turn connected forecasts into operational insight The post From Connections to Meaning: Why Heterogeneous Graph Transformers (HGT) Change Demand Forecasting appeared first on Towards Data Science. Partha Sarkar Go to original source
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Causal ML for the Aspiring Data Scientist
Causal ML for the Aspiring Data Scientist An accessible introduction to causal inference and ML The post Causal ML for the Aspiring Data Scientist appeared first on Towards Data Science. Ross Lauterbach Go to original source
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How to Build a Neural Machine Translation System for a Low-Resource Language
How to Build a Neural Machine Translation System for a Low-Resource Language An introduction to neural machine translation The post How to Build a Neural Machine Translation System for a Low-Resource Language appeared first on Towards Data Science. Kaixuan Chen Go to original source
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Optimizing Data Transfer in Distributed AI/ML Training Workloads
Optimizing Data Transfer in Distributed AI/ML Training Workloads A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems – part 3 The post Optimizing Data Transfer in Distributed AI/ML Training Workloads appeared first on Towards Data Science. Chaim Rand Go to original source
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Why SaaS Product Management Is the Best Domain for Data-Driven Professionals in 2026
Why SaaS Product Management Is the Best Domain for Data-Driven Professionals in 2026 How I use analytics, automation, and AI to build better SaaS The post Why SaaS Product Management Is the Best Domain for Data-Driven Professionals in 2026 appeared first on Towards Data Science. Yassin Zehar Go to original source
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A Case for the T-statistic
A Case for the T-statistic And how it compares to the run-of-the-mill z-score The post A Case for the T-statistic appeared first on Towards Data Science. Aniruddha Karajgi Go to original source
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You Probably Don’t Need a Vector Database for Your RAG — Yet
You Probably Don’t Need a Vector Database for Your RAG — Yet Numpy or SciKit-Learn might meet all your retrieval needs The post You Probably Don’t Need a Vector Database for Your RAG — Yet appeared first on Towards Data Science. Thomas Reid Go to original source
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The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies
The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies How to use n8n with multimodal AI and optimisation tools to help companies with low data maturity accelerate their digital transformation. The post The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies appeared first on Towards Data Science. Samir Saci…
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Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels
Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels Why your final LLM layer is OOMing and how to fix it with a custom Triton kernel. The post Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels appeared first on Towards Data Science. Ryan Pégoud Go to original source
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The Great Data Closure: Why Databricks and Snowflake Are Hitting Their Ceiling
The Great Data Closure: Why Databricks and Snowflake Are Hitting Their Ceiling Acquisitions, venture, and an increasingly competitive landscape all point to a market ceiling The post The Great Data Closure: Why Databricks and Snowflake Are Hitting Their Ceiling appeared first on Towards Data Science. Hugo Lu Go to original source
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Glitches in the Attention Matrix
Glitches in the Attention Matrix A history of Transformer artifacts and the latest research on how to fix them The post Glitches in the Attention Matrix appeared first on Towards Data Science. Jonathan Williford Go to original source
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Under the Uzès Sun: When Historical Data Reveals the Climate Change
Under the Uzès Sun: When Historical Data Reveals the Climate Change Longer summers, milder winters: analysis of temperature trends in Uzès, France, year after year. The post Under the Uzès Sun: When Historical Data Reveals the Climate Change appeared first on Towards Data Science. Marc Polizzi Go to original source
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Optimizing Data Transfer in Batched AI/ML Inference Workloads
Optimizing Data Transfer in Batched AI/ML Inference Workloads A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems – part 2 The post Optimizing Data Transfer in Batched AI/ML Inference Workloads appeared first on Towards Data Science. Chaim Rand Go to original source
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Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example
Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomous vehicle car safety agent running on OpenAI’s GPT 5.2 The post Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example appeared first on Towards Data Science. Vincent Koc Go to…
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Data Science Spotlight: Selected Problems from Advent of Code 2025
Data Science Spotlight: Selected Problems from Advent of Code 2025 Hands-on walkthroughs of problems and solution approaches that power real‑world data science use cases The post Data Science Spotlight: Selected Problems from Advent of Code 2025 appeared first on Towards Data Science. Chinmay Kakatkar Go to original source
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Beyond Prompting: The Power of Context Engineering
Beyond Prompting: The Power of Context Engineering Using ACE to create self-improving LLM workflows and structured playbooks The post Beyond Prompting: The Power of Context Engineering appeared first on Towards Data Science. Mariya Mansurova Go to original source
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HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows
HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows How approximate vector search silently degrades Recall—and what to do about It The post HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows appeared first on Towards Data Science. Partha Sarkar Go to original source
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Measuring What Matters with NeMo Agent Toolkit
Measuring What Matters with NeMo Agent Toolkit A practical guide to observability, evaluations, and model comparisons The post Measuring What Matters with NeMo Agent Toolkit appeared first on Towards Data Science. Mariya Mansurova Go to original source
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Ray: Distributed Computing for All, Part 1
Ray: Distributed Computing for All, Part 1 From single to multi-core on your local PC and beyond The post Ray: Distributed Computing for All, Part 1 appeared first on Towards Data Science. Thomas Reid Go to original source
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Optimizing Data Transfer in AI/ML Workloads
Optimizing Data Transfer in AI/ML Workloads A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems The post Optimizing Data Transfer in AI/ML Workloads appeared first on Towards Data Science. Chaim Rand Go to original source
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EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas
EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas How to build, score, and interpret RFM segments step by step The post EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas appeared first on Towards Data Science. Ibrahim Salami Go to original source
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Deep Reinforcement Learning: The Actor-Critic Method
Deep Reinforcement Learning: The Actor-Critic Method Robot friends collaborate to learn to fly a drone The post Deep Reinforcement Learning: The Actor-Critic Method appeared first on Towards Data Science. Vedant Jumle Go to original source
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Production-Ready LLMs Made Simple with the NeMo Agent Toolkit
Production-Ready LLMs Made Simple with the NeMo Agent Toolkit From simple chat to multi-agent reasoning and real-time REST APIs The post Production-Ready LLMs Made Simple with the NeMo Agent Toolkit appeared first on Towards Data Science. Mariya Mansurova Go to original source
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What Advent of Code Has Taught Me About Data Science
What Advent of Code Has Taught Me About Data Science Five key learnings that I discovered during a programming challenge and how they apply to data science The post What Advent of Code Has Taught Me About Data Science appeared first on Towards Data Science. Jasper Schroeder Go to original source
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Agents Under the Curve (AUC)
Agents Under the Curve (AUC) Towards understanding if your agentic solution is actually better The post Agents Under the Curve (AUC) appeared first on Towards Data Science. Lambert Leong Go to original source
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How to Build an AI-Powered Weather ETL Pipeline with Databricks and GPT-4o: From API To Dashboard
How to Build an AI-Powered Weather ETL Pipeline with Databricks and GPT-4o: From API To Dashboard A step-by-step guide from weather API ETL to dashboard on Databricks The post How to Build an AI-Powered Weather ETL Pipeline with Databricks and GPT-4o: From API To Dashboard appeared first on Towards Data Science. Gustavo Santos Go to…
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The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel
The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel An intuitive, step-by-step look at how Transformers use self-attention to turn static word embeddings into contextual representations, illustrated with simple examples and an Excel-friendly walkthrough. The post The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel appeared first on Towards…
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How Agents Plan Tasks with To-Do Lists
How Agents Plan Tasks with To-Do Lists Understanding the process behind agentic planning and task management in LangChain The post How Agents Plan Tasks with To-Do Lists appeared first on Towards Data Science. Kenneth Leung Go to original source
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Synergy in Clicks: Harsanyi Dividends for E-Commerce
Synergy in Clicks: Harsanyi Dividends for E-Commerce A brief overview of the math behind the Harsanyi Dividend and a real-world application in Streamlit The post Synergy in Clicks: Harsanyi Dividends for E-Commerce appeared first on Towards Data Science. Jacob Ingle Go to original source
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The Machine Learning “Advent Calendar” Day 20: Gradient Boosted Linear Regression in Excel
The Machine Learning “Advent Calendar” Day 20: Gradient Boosted Linear Regression in Excel From Random Ensembles to Optimization: Gradient Boosting Explained The post The Machine Learning “Advent Calendar” Day 20: Gradient Boosted Linear Regression in Excel appeared first on Towards Data Science. angela shi Go to original source
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The Geometry of Laziness: What Angles Reveal About AI Hallucinations
The Geometry of Laziness: What Angles Reveal About AI Hallucinations A story about failing forward, spheres you can’t visualize, and why sometimes the math knows things before we do The post The Geometry of Laziness: What Angles Reveal About AI Hallucinations appeared first on Towards Data Science. Javier Marin Go to original source
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Agentic AI Swarm Optimization using Artificial Bee Colonization (ABC)
Agentic AI Swarm Optimization using Artificial Bee Colonization (ABC) Using Agentic AI prompts with the Artificial Bee Colony algorithm to enhance unsupervised clustering and optimization workflows. The post Agentic AI Swarm Optimization using Artificial Bee Colonization (ABC) appeared first on Towards Data Science. Gal Arav Go to original source
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Six Lessons Learned Building RAG Systems in Production
Six Lessons Learned Building RAG Systems in Production Best practices for data quality, retrieval design, and evaluation in production RAG systems The post Six Lessons Learned Building RAG Systems in Production appeared first on Towards Data Science. Sabrine Bendimerad Go to original source
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4 Ways to Supercharge Your Data Science Workflow with Google AI Studio
4 Ways to Supercharge Your Data Science Workflow with Google AI Studio With concrete examples of using AI Studio Build mode to learn faster, prototype smarter, communicate clearer, and automate quicker. The post 4 Ways to Supercharge Your Data Science Workflow with Google AI Studio appeared first on Towards Data Science. Shuai Guo Go to…
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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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Production-Grade Observability for AI Agents: A Minimal-Code, Configuration-First Approach
Production-Grade Observability for AI Agents: A Minimal-Code, Configuration-First Approach LLM-as-a-Judge, regression testing, and end-to-end traceability of multi-agent LLM systems The post Production-Grade Observability for AI Agents: A Minimal-Code, Configuration-First Approach appeared first on Towards Data Science. Partha Sarkar Go to original source
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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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Spectral Community Detection in Clinical Knowledge Graphs
Spectral Community Detection in Clinical Knowledge Graphs Introduction How do we identify latent groups of patients in a large cohort? How can we find similarities among patients that go beyond the well-known comorbidity clusters associated with specific diseases? And more importantly, how can we extract quantitative signals that can be analyzed, compared, and reused across…
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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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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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Optimizing PyTorch Model Inference on CPU
Optimizing PyTorch Model Inference on CPU Flyin’ Like a Lion on Intel Xeon The post Optimizing PyTorch Model Inference on CPU appeared first on Towards Data Science. Chaim Rand Go to original source
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How to Climb the Hidden Career Ladder of Data Science
How to Climb the Hidden Career Ladder of Data Science The behaviors that get you promoted The post How to Climb the Hidden Career Ladder of Data Science appeared first on Towards Data Science. Greg Rafferty Go to original source
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YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World
YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World A detailed walkthrough of the YOLOv1 architecture and its PyTorch implementation from scratch The post YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World appeared first on Towards Data Science. Muhammad Ardi Go to original source
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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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Build and Deploy Your First Supply Chain App in 20 Minutes
Build and Deploy Your First Supply Chain App in 20 Minutes A factory operator that discovered happiness by switching from notebook to streamlit – (Image Generated with GPT-5.1 by Samir Saci) The post Build and Deploy Your First Supply Chain App in 20 Minutes appeared first on Towards Data Science. Samir Saci Go to original…
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The Architecture Behind Web Search in AI Chatbots
The Architecture Behind Web Search in AI Chatbots And what this means for generative engine optimization (GEO) The post The Architecture Behind Web Search in AI Chatbots appeared first on Towards Data Science. Ida Silfverskiöld Go to original source
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How to Turn Your LLM Prototype into a Production-Ready System
How to Turn Your LLM Prototype into a Production-Ready System The most famous applications of LLMs are the ones that I like to call the “wow effect LLMs.” There are plenty of viral LinkedIn posts about them, and they all sound like this: “I built [x] that does [y] in [z] minutes using AI.” Where:…
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JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability
JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability Benchmarking JSON libraries for large payloads The post JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability appeared first on Towards Data Science. Subha Ganapathi 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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Why AI Alignment Starts With Better Evaluation
Why AI Alignment Starts With Better Evaluation You can’t align what you don’t evaluate The post Why AI Alignment Starts With Better Evaluation appeared first on Towards Data Science. Hailey Quach 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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I Cleaned a Messy CSV File Using Pandas . Here’s the Exact Process I Follow Every Time.
I Cleaned a Messy CSV File Using Pandas . Here’s the Exact Process I Follow Every Time. Stop guessing at data cleaning. Use this repeatable 5-step Python workflow to diagnose and fix the most common data flaws. The post I Cleaned a Messy CSV File Using Pandas . Here’s the Exact Process I Follow Every Time. appeared first on Towards…
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Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It
Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It A real-world analysis of why CrewAI’s hierarchical orchestration misfires—and a practical fix you can implement today. The post Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It appeared first on Towards Data Science. Partha Sarkar 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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Your Next ‘Large’ Language Model Might Not Be Large After All
Your Next ‘Large’ Language Model Might Not Be Large After All A 27M-parameter model just outperformed giants like DeepSeek R1, o3-mini, and Claude 3.7 on reasoning tasks The post Your Next ‘Large’ Language Model Might Not Be Large After All appeared first on Towards Data Science. Moulik Gupta 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 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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Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 2
Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 2 In part 1, we showed how we could leverage HTMX to add interactivity to our HTML elements. In other words, Javascript without Javascript. To illustrate that, we began building a simple chat that would return a simulated LLM response. In this article,…
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Javascript Fatigue: HTMX is all you need to build ChatGPT — Part 1
Javascript Fatigue: HTMX is all you need to build ChatGPT — Part 1 Building a chatbot (almost) without Javascript, only with Python and HTML. The post Javascript Fatigue: HTMX is all you need to build ChatGPT — Part 1 appeared first on Towards Data Science. Benjamin Etienne 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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Deploy Your AI Assistant to Monitor and Debug n8n Workflows Using Claude and MCP
Deploy Your AI Assistant to Monitor and Debug n8n Workflows Using Claude and MCP Use Claude AI to monitor, analyse, and troubleshoot your n8n automation workflows through natural conversation. The post Deploy Your AI Assistant to Monitor and Debug n8n Workflows Using Claude and MCP appeared first on Towards Data Science. Samir Saci Go to…
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The Ultimate Guide to Power BI Aggregations
The Ultimate Guide to Power BI Aggregations Aggregations are one of the most powerful features in Power BI — learn how to leverage this feature to improve the performance of your Power BI solution The post The Ultimate Guide to Power BI Aggregations appeared first on Towards Data Science. Nikola Ilic Go to original source
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Feature Detection, Part 2: Laplace & Gaussian Operators
Feature Detection, Part 2: Laplace & Gaussian Operators Laplace meets Gaussian — the story of two operators in edge detection The post Feature Detection, Part 2: Laplace & Gaussian Operators appeared first on Towards Data Science. Vyacheslav Efimov Go to original source
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Do You Really Need GraphRAG? A Practitioner’s Guide Beyond the Hype
Do You Really Need GraphRAG? A Practitioner’s Guide Beyond the Hype A perspective on GraphRAG design best practices, challenges and learnings The post Do You Really Need GraphRAG? A Practitioner’s Guide Beyond the Hype appeared first on Towards Data Science. Partha Sarkar 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,…