How to implement a training algorithm that finally looks like “real” machine learning The post The Machine Learning “Advent Calendar” Day 4: k-Means in Excel appeared first on Towards Data Science.
Build with Gemini 3 Pro, the best model in the world for multimodal capabilities.
Learn more about Google’s launches, milestones and more from 2025.
Thomas Massie shows the way The post Is there an Epstein connection to our Downsize strategy? appeared first on Downsize DC.
Data leakage is an often accidental problem that may happen in machine learning modeling.
The CEO of the F1 champion-winning team discusses the hurdles he faced reversing McLaren’s negative momentum.
Robust proxies allow you to rotate identities, reach any region, and bypass sophisticated anti-bot systems, all while protecting your infrastructure from blocks and blacklisting.
Announcing: 𝗪𝗪-𝗣𝗚𝗗 — 𝗪𝗲𝗶𝗴𝗵𝘁𝗪𝗮𝘁𝗰𝗵𝗲𝗿 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝗲𝗱 𝗚𝗿𝗮𝗱𝗶𝗲𝗻𝘁 𝗗𝗲𝘀𝗰𝗲𝗻𝘁 I just released WW-PGD, a small PyTorch add-on that wraps standard optimizers (SGD, Adam, AdamW, etc.) and applies an epoch-boundary spectral projection using WeightWatcher diagnostics. Elevator pitch: WW-PGD explicitly nudges each layer toward the Exact Renormalization Group (ERG) critical manifold during training. 𝗧𝗵𝗲𝗼𝗿𝘆 𝗶𝗻 𝘀𝗵𝗼𝗿𝘁 • HTSR critical condition: α ≈ 2 • SETOL ERG condition: trace-log(λ) over the spectral tail = 0 WW-PGD makes these explicit optimization targets, rather than […]
Let’s say an environmental scientist is studying whether exposure to air pollution is associated with lower birth weights in a particular county. They might train a machine-learning model to estimate the magnitude of this association, since machine-learning methods are especially good at learning complex relationships. Standard machine-learning methods excel at making predictions and sometimes provide uncertainties, like confidence intervals, for these predictions. However, they generally don’t provide estimates or confidence intervals when determining whether two variables are related. […]
This article discusses five cutting-edge AutoML techniques and trends that are expected to shape the landscape of highly automated machine learning model building in the 2026 year about to start.