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 ...

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 ...

With deep learning, you start with sample data, deploy the model, and then expose it to the real world. But models that work well on training data often perform poorly on real data.

Training deep learning networks with photo-realistic virtual data created using game development engines has the potential to streamline the learning process and make deep learning solutions ...

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 ...

Fueled by enterprises seeking greater insight from their analytics, deep learning is now seeing widespread adoption. While this artificial intelligence (AI) discipline was first conceived in the late ...

Transfer learning is much faster than training models from scratch, and it requires much less data for the training. Google Cloud AutoML implements deep transfer learning for vision, translation ...

KEY TAKEAWAYS. Deep learning focuses on predicting or classifying data, while generative AI creates new content. (Jump to Section)Common deep learning techniques include CNNs, RNNs, and LSTMs.

Ethical Concerns: Deep learning models can amplify biases present in the training data. If the data used to train a model contains biased information, the model may perpetuate these biases in its ...