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News

Towards Data Science
towardsdatascience.com > dont-just-throw-adam-at-it-misunderstanding-adam-will-cost-you

Don’t Just “Throw Adam at It”: Misunderstanding Adam Will Cost You

10+ hour, 45+ min ago   (1737+ words) You "vibe-coded" the import. Understand Adam's optimization dynamics, why it fails spectacularly, and how to fix it. I spent weeks working on a particularly hard reinforcement learning problem. The research demonstrated similar agents learning comparable tasks. Yet our model was…...

Towards Data Science
towardsdatascience.com > your-ml-experiments-are-a-mess-heres-the-fix

Are Your ML Experiments a Mess? Here’s the Fix

1+ week, 12+ hour ago   (843+ words) A hands-on guide to tracking experiments, logging models, and reproducing results with ML Flow. To ensure that the right models are in production, we need a way to manage various models and versions, track performance metrics, and reproduce results. This…...

Towards Data Science
towardsdatascience.com > rag-was-always-a-temporary-workaround-what-is-next

RAG Was Always a Temporary Workaround. What is Next?

2+ week, 4+ day ago   (944+ words) Why the future of AI memory relies on persistent neural state, not vector databases. Look closely at your favourite RAG system. Very closely. We labelled this “memory.” A more honest name would be: a very elaborate, high-latency game of telephone…...

Google News
towardsdatascience.com > i-pitted-xgboost-against-logistic-regression-on-358-matches-the-boring-model-won

I Pitted XGBoost Against Logistic Regression on 358 Matches. The Boring Model Won.

1+ mon, 11+ hour ago   (1244+ words) A concrete bias–variance lesson: why the smallest model had the best cross-validated fit, and how to know when to reach for the big hammer. So when I lined up five classifiers on the same task and the one-line linear…...

Towards Data Science
towardsdatascience.com > structured-outputs-with-llms-json-mode-function-calling-and-when-to-use-each

Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each

1+ mon, 1+ week ago   (664+ words) Getting reliable, readable responses out of your LLM, and knowing which tool to reach for In my latest posts, we’ve talked a lot about popular techniques for optimizing the performance and cost of AI applications, like response streaming or prompt…...

Towards Data Science
towardsdatascience.com > proteins-a-mosaic-pattern-to-rule-them-all

Proteins: A Mosaic Pattern to Rule Them All?

1+ mon, 1+ week ago   (429+ words) An introduction to the Mosaic Q model and to the tools you can use for its quantification and visualization. In the next images we see an overview of the analysis. Here is how the mosaic, its quantification via Q, and…...

Towards Data Science
towardsdatascience.com > vision-llms-are-pdf-parsers-too-reading-charts-and-diagrams-for-rag

Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG

1+ mon, 2+ week ago   (860+ words) Enterprise Document Intelligence [Vol.1 #5quater] – The other parsers read the words on a page. A vision model also reads the pictures Show a PDF parser a chart and it sees an empty box. The text engines, native or cloud or local,…...

Google News
towardsdatascience.com > parse-pdfs-for-rag-locally-with-docling-rich-tables-no-cloud-upload

Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload

1+ mon, 2+ week ago   (1358+ words) 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 richest parser you can buy reads the table, the scan, and the text trapped…...

Towards Data Science
towardsdatascience.com > how-to-train-a-scoring-model-in-the-age-of-artificial-intelligence

How to Train a Scoring Model in the Age of Artificial Intelligence

1+ mon, 2+ week ago   (1638+ words) A structured methodology for comparing candidate models, testing stability, and selecting a robust final score. All code used in this section is available on GitHub. The business logic and modeling functions are located in the src/selection directory, specifically in…...

Towards Data Science
towardsdatascience.com > predicting-solar-flares

Using Transformers to Forecast Incredibly Rare Solar Flares

2+ mon, 2+ week ago   (856+ words) How ML can change for rare events Machine Learning forecasting fundamentally changes whenever we try to predict a very rare event. We must fundamentally shift what we are modelling to focus on tail events. From model performance metrics and target…...