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Learning Theory </span> </a> </li> </ul> </nav> </li> <li class=md-nav__item> <a href=../../%E5%90%8E%E8%AE%B0/ class=md-nav__link> <span class=md-ellipsis> 后记 </span> </a> </li> </ul> </nav> </div> </div> </div> <div class="md-sidebar md-sidebar--secondary" data-md-component=sidebar data-md-type=toc> <div class=md-sidebar__scrollwrap> <div class=md-sidebar__inner> <nav class="md-nav md-nav--secondary" aria-label=目录> <label class=md-nav__title for=__toc> <span class="md-nav__icon md-icon"></span> 目录 </label> <ul class=md-nav__list data-md-component=toc data-md-scrollfix> <li class=md-nav__item> <a href=#_1 class=md-nav__link> <span class=md-ellipsis> 课程简介 </span> </a> </li> <li class=md-nav__item> <a href=#_2 class=md-nav__link> <span class=md-ellipsis> 课程资源 </span> </a> </li> <li class=md-nav__item> <a href=#_3 class=md-nav__link> <span class=md-ellipsis> 资源汇总 </span> </a> </li> </ul> </nav> </div> </div> </div> <div class=md-content data-md-component=content> <article class="md-content__inner md-typeset"> <h1 id=umich-eecs-498-007-598-005-deep-learning-for-computer-vision>UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision</h1> <h2 id=_1>课程简介</h2> <ul> <li>所属大学：UMich</li> <li>先修要求：Python基础，矩阵论(熟悉矩阵求导即可)，微积分</li> <li>编程语言：Python</li> <li>课程难度：🌟🌟🌟🌟</li> <li>预计学时：60～80 小时</li> </ul> <p>UMich 的 Computer Vision 课，课程视频和作业质量极高，涵盖的主题非常全，同时 Assignments 的难度由浅及深，覆盖了 CV 主流模型发展的全阶段，是一门非常好的 Computer Vision 入门课。</p> <p>你在每个 Assignment 里会跟随 Handouts 搭建与训练 Lectures 中提到的模型/框架。</p> <p>你不需要有任何的深度学习框架的使用经验，在开始的 Assignment 里，这门课会从零开始教导每个学生如何使用 Pytorch，后续也可以当成工具书，随时翻阅。</p> <p>同时由于每个 Assignment 之间涉及到的主题都不同，你在递进式的 Assignment 中不仅可以亲身体会到 CV 主流模型的发展历程，领略到不同的模型和训练的方法对最终效果/准确率的影响，同时也能 Hands On 地实现它们。</p> <p>在 A1 中，你会学习 Pytorch 和 Google Colab 的使用。</p> <p>在 A2 中你会亲自搭建 Linear Classifier 以及一个两层的神经网络，最后你有机会亲自接触 MNIST 数据集并在此基础上训练并评估你搭建起的神经网络。</p> <p>在 A3 中，你会接触到最为经典的 Convolutional Neural Network (A.K.A. CNN)，亲自感受卷积神经网络的魅力。</p> <p>而在 A4 中，你将实际触及搭建物体检测模型的全流程，同时跟随 Handout 实现两篇论文中的 One-Stage Detector 和 Two-Stage Detector。</p> <p>到了 A5，就是从 CNN 到 RNN 的时刻了，你将有机会亲自搭建起两种不同的基于注意力的模型，RNNs (Vanilla RNN &amp; LSTM) 和大名鼎鼎的 Transfomer。</p> <p>在最后一个 Assignment（A6）中，你将有机会实现两种更为 Fancy 的模型，VAE 和 GAN，并应用在 MINST 数据集上。最后，你会实现网络可视化和风格迁移这两个非常酷炫的功能。</p> <p>在 Assignments 之外，你还可以自己实现一个 Mini-Project，亲自搭建起一个完整的深度学习 Pipeline，具体可以参考课程主页。</p> <p>课程所涉及的资源，如 Lectures/Notes/Assignments 都是开源的，美中不足的是 Autograder 只对本校 Enrolled 的学生开放，但因为在提供的 <code>*.ipynb</code>（也就是 Handout） 中已经可以确定实现的正确性，以及预期的结果，所以我个人觉得 Autograder 的缺失没有任何影响。</p> <p>值得一提的是，这门课的主讲教授 Justin Johnson 正是 Fei-Fei Li 的博士毕业生，现在在 UMich 当 Assistant Professor。</p> <p>而现在开源的 2017 年版本的 Stanford CS231N 的主讲人就是 Justin Johnson。</p> <p>同时因为 CS231N 主要是由 Justin Johnson 和 Andrej Karpathy 建设起来的，这门课也沿用了 CS231N 的一些材料，所以学过 CS231N 的同学可能会觉得这门课的某些材料比较熟悉。</p> <p>最后，我推荐每一个 Enroll 这门课的同学都去看一看 Youtube 上面的 Lectures，Justin Johnson 的讲课方式和内容都非常清晰和易懂，是非常棒的参考。</p> <h2 id=_2>课程资源</h2> <ul> <li>课程网站：<a href=https://web.eecs.umich.edu/~justincj/teaching/eecs498/WI2022/ >https://web.eecs.umich.edu/~justincj/teaching/eecs498/WI2022/</a></li> <li>课程视频：<a href="https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r">https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r</a></li> <li>课程教材：仅有推荐教材，链接：<a href=https://www.deeplearningbook.org/ >https://www.deeplearningbook.org/</a></li> <li>课程作业：见课程主页，6 个 Assignment 和一个 Mini-Project</li> </ul> <h2 id=_3>资源汇总</h2> <p>@Michael-Jetson 本人所做的二三十万字的笔记（并没有包括作业等），可以当做一个参考<a href=https://github.com/Michael-Jetson/ML_DL_CV_with_pytorch>Michael-Jetson/ML_DL_CV_with_pytorch</a></p> <aside class=md-source-file> <span class=md-source-file__fact> <span class=md-icon title=最后更新> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 24 24"><path d="M21 13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6 0-.8l-1.3-1.3c-.1-.1-.2-.2-.4-.2m-1.9 1.8-6.1 6V23h2.1l6.1-6.1-2.1-2M12.5 7v5.2l4 2.4-1 1L11 13V7h1.5M11 21.9c-5.1-.5-9-4.8-9-9.9C2 6.5 6.5 2 12 2c5.3 0 9.6 4.1 10 9.3-.3-.1-.6-.2-1-.2s-.7.1-1 .2C19.6 7.2 16.2 4 12 4c-4.4 0-8 3.6-8 8 0 4.1 3.1 7.5 7.1 7.9l-.1.2v1.8Z"/></svg> </span> <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">June 16, 2024</span> </span> </aside> <!-- Insert generated snippet here --> <script src=https://giscus.app/client.js data-repo=PKUFlyingPig/cs-self-learning data-repo-id=R_kgDOGP67ng data-category=Announcements data-category-id=DIC_kwDOGP67ns4COM9Q data-mapping=title data-reactions-enabled=1 data-emit-metadata=0 data-input-position=top data-theme=light_protanopia data-lang=zh-CN data-loading=lazy crossorigin=anonymous async>
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