Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test accuracy is very low, the model highly overfits the training dataset set ...

Reinforcement learning frames trading as a sequential decision-making problem, where an agent observes market conditions, ...

What is overfitting and underfitting in machine learning? What is Bias and Variance? Overfitting and Underfitting are two common problems in machine learning and Deep learning. If a model has low ...

Machine learning is reshaping the way portfolios are built, monitored, and adjusted. Investors are no longer limited to ...

A research team has introduced a lightweight artificial intelligence method that accurately identifies wheat growth stages ...

Ruyi Ding (Northeastern University), Tong Zhou (Northeastern University), Lili Su (Northeastern University), Aidong Adam Ding (Northeastern University), Xiaolin Xu (Northeastern University), Yunsi Fei ...

By transferring temporal knowledge from complex time-series models to a compact model through knowledge distillation and attention mechanisms, the ...

Duke University engineers are using artificial intelligence to do something scientists have chased for centuries; turn messy, ...

According to TII’s technical report, the hybrid approach allows Falcon H1R 7B to maintain high throughput even as response ...

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...