MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.
借助OpenCV和CUDA实现GPU加速的计算机视觉 该书共计12章，涵盖以下内容： 1.了解如何从CUDA程序访问GPU设备属性和功能； 2.了解如何加快搜索和排序算法； 3.检测图像中的线条和圆形等形状； 4.使用算法探索对象跟踪和检测； 5.在Jetson TX1中使用不同的视频分析技术处理视频； 6.从PyCUDA程序访问GPU设备属性； 7.了解内核执行的工作原理。 每章均配有代码，并录制了10个视频教程。 作为该领域最新的相关开发教程，非常值得参考。
