CS231n--笔记（2）课程简介

1. Course Description

课程简介

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 focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge.

计算机视觉已经在我们的社会中无处不在，应用于搜索，图像理解，应用程序，绘图，医学，无人机和自动驾驶汽车。许多这些应用程序的核心是视觉识别任务，例如图像分类，定位和检测。神经网络（又名“深度学习”）方法的最新发展极大地提高了这些最先进的视觉识别系统的性能。本课程深入探讨深度学习架构的细节，重点是学习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，学生将学习如何实施，训练和调试自己的神经网络，并详细了解计算机视觉领域的前沿研究。最终任务将涉及训练数百万参数卷积神经网络并将其应用于最大图像分类数据集（ImageNet）。我们将专注于教授如何设置图像识别问题，学习算法（例如反向传播），培训和微调网络的实用工程技巧，并指导学生完成动手作业和最终课程项目。本课程的大部分背景和材料将来自ImageNet挑战赛。

2. 课程作业和实践

 2.1 Assignment Details

作业jupyter notebook中文解析请订阅个人github--->https://github.com/HaooWang/CNNs-CS231n

作业共有三个，涵盖了计算机视觉以及机器学习的绝大部分内容，也可查看源网页地址See the Assignment Page for more details on how to hand in your assignments.

2.2 Course Project Details

课程包含了实践项目，可通过查看网址获得更多咨询See the Project Page for more details on the course project.

3 课程内容

3.1 模块一 浅层神经网络及其应用

网址：http://cs231n.github.io/，http://cs231n.github.io/classification/等

3.2 模块二  深度神经网络

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