CS231n: Convolutional Neural Networks for Visual Recognition 2016

Course Description Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will fo

计算机视觉在我们社会中已变得无处不在，其应用涵盖了搜索、图像理解、应用程序、地图制作、医学、无人机以及自动驾驶汽车等多个领域。在这些应用中，视觉识别任务，如图像分类、定位和检测，是核心所在。近年来，神经网络（亦称“深度学习”）方法的进步显著提升了这些最先进视觉识别系统的性能。本课程深入探讨了深度学习架构的细节，重点在于学习针对这些任务的端到端模型，尤其是图像分类。在为期10周的课程中，学生将学习实现、训练和调试自己的神经网络，并对计算机视觉领域的尖端研究获得深入理解。最终作业将涉及训练一个拥有数百万参数的卷积神经网络，并将其应用于最大的图像分类数据集（ImageNet）。我们将深入探讨（...）