If you have ever tried downloading a local LLM, you have probably seen model names that look like this: Qwen3.8-27B-A3B-It-2507-gguf-q2ks-mixed-AutoRound At first, it looks like meaningless technical shorthand. It isn’t! Every part of that name tells you something about the model: how large it is...
Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks
More informed decision-making through uncertainty quantification
The post Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks appeared first on Towards Data Science.
A clean run proves the process executed. It says nothing about what the pipeline learned, from which rows, in what state, or whether the saved result can be trusted anywhere else.
5 AI Skills That Will Keep Data Scientists Relevant in 2027
What each one solves, and runnable code you can paste into a notebook.
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5 Best Local LLMs You Can Run on a Mac mini in 2026
Proprietary models are amazing! But sometimes what is of importance is configurability rather than raw power. This has led to the emergence of locally hosted models. The Mac mini has emerged as a surprisingly capable machine for running AI locally. With Apple Silicon, enough unified memory, and too...
Enterprise Document Intelligence [Vol.1 #B2] - The FAQ inverts every brick of the standard RAG pipeline. Parsing is trivial, retrieval doubles as a cache, and few-shot prompting becomes a retrieval problem too
The post FAQ as RAG: When You Get to Design the Corpus appeared first on Towards Data Scie...
A framework for building RAG pipelines that introduces complexity in response to observed failure modes, from lexical and hybrid search to reranking and agentic information seeking
The post Why RAG Complexity Should Be Earned appeared first on Towards Data Science.
7 Python Mistakes Beginners Make (And What to Do Instead)
It's about the mistakes that make a running program wrong. Below are seven of them. For each one you get the hidden cause, plus the first thing worth checking.
Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves
Enterprise Document Intelligence [Vol.1 #B1] - Three sources of one problem. User typos, fast-typing transcription noise, OCR character errors. Classical spell-check handles one of them. Embeddings carry the rest
The post Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves appear...
4 Claude Skills Every Data Scientist Needs in 2026
Four skills worth adding to your workflow today if you don't want to be left behind
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We use the equation all the time. But where did it actually come from?
The post The Sigmoid Function: From 'e' to Neural Networks appeared first on Towards Data Science.
I Trained Six Models for Fraud Detection, and the Best One Isn't in Production
What a final-year project taught me about the gap between evaluation metrics and production decisions
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Learn Vectorized Thinking in Python Through Examples
In this article, you will learn how to think in terms of vectorized operations using NumPy, replacing slow Python loops with efficient array-level computations. Topics...
Bagging hits a wall no amount of trees can break — here's the equation that explains why, and the experiment that proves it
The post Why Random Forest Needs to Be This Random appeared first on Towards Data Science.
How to Format Your TDS Draft: A New and Improved Guide
Everything you need to know about creating a draft on our Contributor Portal
The post How to Format Your TDS Draft: A New and Improved Guide appeared first on Towards Data Science.
Learn how Python dataclasses go beyond reducing boilerplate with custom fields, validation, computed attributes, immutability, and memory optimization techniques.
Comparing Local Tool Calling: Gemma 4 vs. Llama 3 vs. Mistral
In this article, you will learn how Gemma 4, Llama 3, and Mistral implement tool calling locally, and what trade-offs each model family presents for...