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 ...

Waymo put it best this past December when the company noted that “deep learning identifies correlations in the training data, but it arguably cannot build causal models by purely observing ...

Using this synthetic data, Uber sped up its neural architecture search (NAS) deep-learning optimization process by 9x. In a paper published on arXiv, the team described the system and a series of ...

Deep learning models have been given proper training by using a large subset of labeled data and neural network architectures, which in turn helps these models to learn directly from that data ...

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 ...

More recently GPT-3, a language model that uses deep learning to produce humanlike text, benefited from training on hundreds of billions of words of online text.

What is deep learning? This branch of AI programming works to create computer systems inspired by the way the brain works. These systems are especially good at dealing with large amounts of data ...