I’m feeling hungry, so I go to my kitchen and turn on the stove. I inadvertently have the handle of the frying pan sitting above the flame and when I go to pick up the pan my entire hand melts off in an instant.
Oscar's thoughts too large for a tweet
And Charles Hoskinson is a snake oil salesman submitted by /u/LivingShine83 [link] [Kommentare]
Hi everyone, I work for a major berry company, and a large part of my role involves forecasting total industry crop volumes (weekly harvest/production forecasts) as well as future pricing. I'm relatively new to ML-based forecasting. This is only my second professional role, and I have a bachelor's degree in Information Systems with a few machine learning courses under my belt, but I'm definitely not a forecasting expert. For crop forecasting, I've been working with USDA and other industry datasets. I started with SARIMA models and have recently been experimenting with XGBoost and Holt-Winters methods to compare performance. I'm looking for recommendations on: Libraries/frameworks that are commonly used for production-grade time series forecasting Models that work well for agricultural production forecasting Approaches for forecasting commodity/produce pricing Feature engineering ideas (weather, seasonality, acreage, imports, etc.) Any papers, blogs, or resources that would be useful Most of the data is weekly and highly seasonal, with weather and supply conditions playing a major role. Any suggestions, lessons learned, or pointers from people working in forecasting would be greatly appreciated. submitted by /u/foreigneverythingg [link] [Kommentare]
Threat intelligence report: SearchJack. Research by MalExt Sentry.
Visualize decision trees in Python. Contribute to mljar/supertree development by creating an account on GitHub.
Human review of Terraform plans doesn’t scale, and AI review isn’t deterministic enough for production. Here’s how we use conftest to auto-apply plans that meet an explicit, testable policy.
