Training deep learning models with minimal data remains a challenging yet exciting field. By leveraging techniques such as data augmentation, transfer learning, and synthetic data generation, it ...

Deep learning requires ample data and training time. But while application development has been slow, recent successes in search, advertising, and speech recognition have many companies clamoring ...

Semi-Supervised Learning: Deep learning models receive both unlabeled and labeled data in their training set, requiring them to simultaneously give expected outputs and infer outputs based on ...

Overfitting: Deep learning models, especially when trained on small or biased datasets, are prone to overfitting, where they perform well on training data but poorly on unseen data. Ethical ...

While deep learning-based FAS models have made remarkable progress, the study highlights critical challenges that undermine ...

Needs Lots of Data: Deep learning models need vast amounts of labeled data to perform well, which can be expensive or difficult to obtain. Strains Resources: Training these models requires ...

Deep learning applications. Deep learning can be used in a wide variety of applications, including: Image recognition: To identify objects and features in images, such as people, animals, places ...