All popular deep learning frameworks will be supported and scale across multiple nodes.
所有常用的深度学习框架将在多个节点间获得支持和扩展。
Another important avenue to understand deep learning is to better measure their performance.
另一个理解深度学习的重要途径是更好地衡量它们的表现。
These applications can benefit from recent advances in computer vision and deep learning.
这些应用可以从近年来的计算机视觉和深度学习发展中受益。
Using deep learning to generate music is also a popular application.
使用深度学习生成音乐是一项广受欢迎的应用。
Meet all your growing deep learning capacity and performance requirements.
满足您所有不断增长的深度学习容量和性能需求。
Discover the techniques used to create computer vision applications with deep learning.
发现用深度学习创建计算机视觉应用所使用的技术。
Accelerate your deep learning work with this efficient compute solution.
利用这个高效的计算解决方案加速深度学习工作。
But these students have been ableto produce amazing deep learning results.
但是这些学生已经能够做出惊人的深度学习成果。
Use this deep learning inference kit to build computer vision solutions.
使用此深度学习推理套件构建计算机视觉解决方案。
Use massive big data and leading deep learning algorithms to optimize images.
使用海量大数据和领先的深度学习算法优化图片。
In this chapter, we explore common deep learning optimization algorithms in depth.
本章将详细介绍深度学习中常用的优化算法。
Most deep learning workloads consists of both training and inference.
大多数深度学习工作负载包含训练和推断。
Accelerate the compute-intensive parts of deep learning applications and frameworks.
加速深度学习应用程序和框架的计算密集部分。