Towards Data Science
Jul 6
How to Run End-to-End Tests with Claude Code
Increase the effectiveness of your coding agents through end-to-end testing. The post How to Run End-to-End Tests with Claude Code appeared first on Towards Data Science .
Autoencoders vs. PCA: I Rigged the Test and PCA Still Won
The article titled "Autoencoders vs. PCA: I Rigged the Test and PCA Still Won" discusses a comparative study between autoencoders and Principal Component Analysis (PCA). It highlights that a theoretical advantage claimed for autoencoders did not hold up when tested against a real benchmark, leading to PCA outperforming the autoencoder in this scenario.
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Towards Data Science
Sep 22
Break Your Own RAG Pipeline Before Users Do
The article discusses a small adversarial test set designed to detect retrieval failures in Retrieval-Augmented Generation (RAG) pipelines that typical evaluation sets might miss. It emphasizes the importance of proactively testing your own RAG system to uncover hidden weaknesses before users encounter them. By using this targeted test set, developers can improve the reliability and robustness of their RAG models.
Towards Data Science
Jun 3
I Spent May Evaluating Different Engines for OCR
Testing fourteen engines on ninety-three human documents The post I Spent May Evaluating Different Engines for OCR appeared first on Towards Data Science .
Towards Data Science
Jul 12
RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each
Two techniques, two different problems, and why the question is not really "which one wins" The post RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each appeared first on Towards Data Science .
Towards Data Science
Jul 10
RAG Was Always a Temporary Workaround. What is Next?
Vector databases are a temporary bridge. Discover why the next AI infrastructure revolution relies on persistent neural state and strict latency budgets, not on vector databases.
Towards Data Science
Aug 28
Why Claude Code Time Estimates Are Poor
The article titled "Why Claude Code Time Estimates Are Poor" discusses the challenges and shortcomings of using Claude, an LLM, for estimating code development time. It highlights how these estimates can be unreliable and offers insights into improving communication when working with LLM programming tools.
Towards Data Science
4d ago
Towards Spec-Driven Test Automation: Part 2
The article titled "Towards Spec-Driven Test Automation: Part 2" discusses the implications of a single test run and what it actually proves. It appears on the Towards Data Science platform.
Towards Data Science
Sep 19
AI Made Me 5x Faster. It Also Made Me 5x Worse at My Job.
One near miss, four months of running agents, and the question almost nobody is asking: what are you supposed to do while the AI writes the code? The post AI Made Me 5x Faster. It Also Made Me 5x Wors...
Towards Data Science
Sep 21
Your AI Assistant Wrote the Code. Who Checked the Defaults?
Five scikit-learn defaults that deserve a closer look before your next model reaches production The post Your AI Assistant Wrote the Code. Who Checked the Defaults? appeared first on Towards Data Scie...
arXiv Machine Learning
Aug 11
A solvable high-dimensional model where nonlinear autoencoders learn structure invisible to PCA while test loss misaligns with generalization
arXiv:2602. 10680v2 Announce Type: replace-cross Abstract: Many real-world datasets contain hidden structure that cannot be detected by simple linear correlations between input features.
Towards Data Science
Jul 14
A Gentle Introduction to Autoencoders & Latent Space
Introduction Heavy computation is a well-known problem in various ML algorithms today, especially when generative AI is applied to text, images, and other unstructured data. One of the principal approaches to mitigate this problem is to compress input data into a lower-dimensional representation while preserving the main context.
Towards Data Science
Sep 17
When Does Graph RAG Actually Add Value? A Hands-On Experiment
I built four AI retrieval architectures on a laptop and benchmarked them against the same set of documents and questions. Here’s what the results taught me about the trade-offs between plain RAG, grap...