Deep learning, used to classify images, recognize voices and analyze video images, is emerging as the next big wave of artificial intelligence.

Deep-learning applications require embedded systems able to provide high computing capabilities, flexibility, availability of advanced peripherals, and high performance while operating in real time.

Deep learning has advanced rapidly, driving breakthroughs in image recognition, natural language processing, and autonomous ...

The Wenxin X1.1 Deep Thinking Model Goes Live, Achieving SOTA on Multiple Benchmark Tests ...

Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their ...

Deep learning may be bumping up against conceptual limits as a model of intelligence, but opportunities to apply it to transform industries and enact massive real-world change still abound.

As the world grapples with climate change and dwindling fossil fuel reserves, biodiesel emerges as a promising renewable ...

Nvidia wants the artificial intelligence and deep learning markets badly. It launched its 15-billion transistor Tesla P100 chip in April for deep-learning applications. And now it is announcing ...

How deep learning enhances rule-based machine vision in quality and process control inspection applications. How edge learning compares to deep learning in machine-vision applications. Which ...

Computer vision, another application of deep learning, is streamlining microbiological testing and breast cancer screening.