In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...

Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and economic uncertainty post-pandemic. Machine learning models can improve forecasting ...

Recent study reveals machine learning's potential in predicting the strength of carbonated recycled concrete, paving the way ...

Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of uncertainty.

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...

Relating brain activity to behavior is an ongoing aim of neuroimaging research as it would help scientists understand how the brain begets behavior — and perhaps open new opportunities for ...