课程介绍：

      计算机视觉在社会中已经逐渐普及，并广泛运用于搜索检索、图像理解、手机应用、地图导航、医疗制药、无人机和无人驾驶汽车等领域。而这些应用的核心技术就是图像分类、图像定位和图像探测等视觉识别任务。近期神经网络（也就是“深度学习”）方法上的进展极大地提升了这些代表当前发展水平的视觉识别系统的性能。
      本课程将深入讲解深度学习框架的细节问题，聚焦面向视觉识别任务（尤其是图像分类任务）的端到端学习模型。在10周的课程中，学生们将会学习如何实现、训练和调试他们自己的神经网络，并建立起对计算机视觉领域的前沿研究方向的细节理解。最终的作业将包括训练一个有几百万参数的卷积神经网络，并将其应用到最大的图像分类数据库（ImageNet）上。我们将会聚焦于教授如何确定图像识别问题，学习算法（比如反向传播算法），对网络的训练和精细调整（fine-tuning）中的工程实践技巧，指导学生动手完成课程作业和最终的课程项目。本课程的大部分背景知识和素材都来源于ImageNet Challenge竞赛。

主讲人：
        
     李飞飞，斯坦福大学计算机科学系副教授。担任斯坦福大学人工智能实验室和视觉实验室主任，主要研究方向为机器学习、计算机视觉、认知计算神经学。她在TED上的演讲，如何教计算机理解图片。

课程目录：

        CS231n课程资源主要由授课视频与PPT，授课知识详解笔记和课程作业三部分组成。其中：
       1.授课视频15课。每节课时约1小时左右，每节课一份PPT。
       2.授课知识详解笔记共9份。光看课程视频是不够的，深入理解课程笔记才能比较扎实地学习到知识。
       3.课程作业3次。其中每次作业中又包含多个小作业，完成作业能确保对于课程关键知识的深入理解和实现。

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