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Enterprise Document Intelligence [Vol.1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine. No key, no per-page bill, nothing leaves the building The post Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload appeared first on Towards Da...
Let's practice data science thinking through a probability problem The post Solving the 3Blue1Brown String Probability Problem (Without AI) appeared first on Towards Data Science.
Enterprise Document Intelligence [Vol.1 #5bis] - The same relational tables. Native table cells. OCR for scanned pages and images. Captions and headings without regex. The post When PyMuPDF Can’t See the Table: Parse PDFs for RAG with Azure Layout appeared first on Towards Data Science.
For nearly a decade, this part of neural networks barely changed. DeepSeek is trying to reinvent it. The post Why Decade-Old Residual Connections Still Power All of AI (And Why That’s a Problem) appeared first on Towards Data Science.
Claude can now write its own harness on the fly, custom-built for the task at hand. The post A Harness for Every Task: Putting a Team of Claudes on One Job appeared first on Towards Data Science.
I tried to make my ETL pipeline production-ready. Three things broke. Each one taught me something scripting alone never could. The post I Thought Data Engineering Was Just Writing Scripts. I Was Wrong. appeared first on Towards Data Science.
A story about a broken printer, visual inductive bias, and why the race endedin a tie. The post Is Language Visual? An Experiment with Chinese Characters appeared first on Towards Data Science.
The true bottleneck was never the analysis. The post BI Is Dead, Long Live BI appeared first on Towards Data Science.
Enterprise Document Intelligence [Vol.1 #5B] - One PDF in, a relational set of DataFrames out: lines, pages, TOC, images, cross-references, captions, spans, and a parsing summary The post Stop Returning Flat Text from a PDF: The Relational Shape RAG Needs appeared first on Towards Data Science.
Take the next step to building real workflows with Spark on your laptop The post PySpark for Beginners: Beyond the Basics appeared first on Towards Data Science.
Why “average utilization” lies about how full your GPUs really are The post When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI appeared first on Towards Data Science.
An in-depth performance test comparing Nucs and Choco The post NuCS vs Choco: A Pure-Python Constraint Solver Meets a JVM Veteran appeared first on Towards Data Science.
Improve coding agent productiveness with refactored code The post How to Refactor Code with Claude Code appeared first on Towards Data Science.
A structured methodology for comparing candidate models, testing stability, and selecting a robust final score The post How to Train a Scoring Model in the Age of Artificial Intelligence appeared first on Towards Data Science.
Enterprise Document Intelligence [Vol.1 #5A] - Document signals (metadata, native TOC, source software) and page-level content (text vs scans, tables, images, columns, page profile) The post Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality appeared first on Towards Data Science.
An intuitive introduction to reasoning with uncertainty, from directed Bayesian networks to undirected Markov networks and weighted logical rules. The post Bayesian Networks and Markov Networks: An Intuitive Guide to Structured Uncertainty appeared first on Towards Data Science.
A quick guide to separating Physical AI from world models, embodied AI, physics AI, and digital twins The post Physical AI: What It Is and What It Is Not appeared first on Towards Data Science.
Enterprise Document Intelligence [Vol.1 #4bis] - A coauthor note on the brick-by-brick pitfalls that justified the four-brick split, before Part II walks the fixes The post 10 Common RAG Mistakes We Keep Seeing in Production appeared first on Towards Data Science.
CPUs, GPUs, TPUs, and NPUs The post The Hardware That Makes AI Possible appeared first on Towards Data Science.
Stop re-computing the same context. Learn how to build a C++ runtime with copy-on-fork KV snapshots to eliminate redundant LLM prefills in multi-agent pipelines. The post Prefill Once, Fan Out: KV Snapshot Sharing for Multi-Agent LLM Pipelines appeared first on Towards Data Science.
Follow this framework to build a project that will impress hiring managers The post The Exact ML Project I’d Build to Get Hired in 2026 appeared first on Towards Data Science.
Building an ML football forecaster in R The post Can Machine Learning Predict the World Cup? appeared first on Towards Data Science.
This is how LLMs are used today to increase precision in recommendation systems The post Increase Recommendation Systems’ Precision with LLMs, Using Python appeared first on Towards Data Science.
Quantum Machine Learning promises powerful new ways of processing information, but quantum states are extraordinarily fragile. In this article, we explore why quantum information is so difficult to protect, how noise and decoherence introduce errors, and the fundamental ideas behind Quantum Error Co...
Get the most out of Claude Code with these four techniques The post 4 New Techniques to Maximize Claude Code appeared first on Towards Data Science.
What Fourier analysis misses The post Sequential Fitting: A Different Perspective on the Spectral Bias of Neural Networks appeared first on Towards Data Science.
The clipping bug has lived in every 3D simulation pipeline for three decades. Here is exactly why it happens, how the math breaks, and how swapping one equation fixes it; as well as the python code to see it for yourself! The post The Polynomial That Fixed 30 Years of Cloth Simulation appeared first...
Because the alternative is much too dangerous The post We Should Train AI to Betray Its Users appeared first on Towards Data Science.
An introduction to multi-agent systems The post Building a Multi-Agent System in Python appeared first on Towards Data Science.
My approach to guiding the choice between Eppo and Statsig, and the lessons learned The post Picking an Experimentation Platform: A Retrospective appeared first on Towards Data Science.
Building a forecast from Elo, Poisson, and 10,000 simulations The post Who Will Win the 2026 Soccer World Cup? appeared first on Towards Data Science.
what it costs, what it gains and the three mistakes that I make The post My SciPy ODE Solver Was Killing My Bayesian Inference: A Cosmologist’s Honest Account of Discovering Diffrax appeared first on Towards Data Science.
I got tired of copying files into an AI chat just to get feedback. So I built a pure Python MCP server that gives AI tools direct access to my local project—no frameworks, no dependencies. It runs over stdio for local use and switches to HTTP/SSE for concurrent clients with a single flag. The result...
How a simple choice shapes exploration, safety, and efficiency The post The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy appeared first on Towards Data Science.
Using DSPy to automatically create, evaluate, and optimize your prompts The post Automate Writing Your LLM Prompts appeared first on Towards Data Science.
Python tutorial for fine-tuning a Mistral Small 3.1 on an imbalanced training set to classify 15 emotions in social media communication The post How to Fine-Tune an SLM for Emotion Recognition appeared first on Towards Data Science.
Abacus.AI and the case for unified AI workflows The post How to Navigate the Shift from Prompt-Based Tools to Workflow-Driven AI appeared first on Towards Data Science.
In Part 1 of this series, we introduced Chronos-2, a time-series foundation model. We got our hands dirty by walking through a real case study and saw what Chronos-2 can do straight out of the box, with no training. But as we noted at the end of Part 1, zero-shot isn’t always enough. In cases […] Th...
When images, mosaics, and data cubes exist in abundance, but field labels are expensive, rare, and imperfect. The post Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce appeared first on Towards Data Science.
Understanding how FPN allows deep learning models detecting small objects and how to implement it from scratch The post FPN Paper Walkthrough: Leveraging the Internal Pyramid appeared first on Towards Data Science.
A look at the real-world value of online graduate AI programs, combining hard data with firsthand experience of a big tech machine learning engineer The post Is an Online Master’s Degree in AI a Good Idea? appeared first on Towards Data Science.
Testing fourteen engines on ninety-three human documents The post I Spent May Evaluating Different Engines for OCR appeared first on Towards Data Science.
AI does not decide who gets fired. Companies do. The post Why AI Is NOT Stealing Your Job appeared first on Towards Data Science.
A comprehensive guide to optimizing LLM inference by eliminating padding overhead with hardware-aware sequence packing. The post I Built a C++ Backend So My GPU Would Stop Eating Air appeared first on Towards Data Science.
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science.
The barriers to building have collapsed. That shifts the bottleneck to ownership, validation, taste, and deciding what should actually exist The post Code Is Cheap. Engineering Judgement Is Now the Scarce Resource appeared first on Towards Data Science.
Three free ways to quickly deploy a static web app that anyone can access The post From Local App to Public Website in Minutes appeared first on Towards Data Science.
Enterprise Document Intelligence [Vol.1 #4] - A diagnostic across PDFs and questions, and a map of the techniques the rest of the series will cover The post From Regex to Vision Models: Which RAG Technique Fits Which Problem appeared first on Towards Data Science.
Exploratory data analysis on the US Census Dataset The post Exploring Income Patterns with Python Pandas, Matplotlib, and Seaborn appeared first on Towards Data Science.
Enterprise Document Intelligence [Vol.1 #3] - Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability frameworks) solves the wrong problem, and what to use instead The post RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem appeared first on Towards Data Sc...
