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 .\install.ps1 -ScoopDir 'C:\Scoop'
 </code></pre> <h2 id=using-scoop>Using Scoop</h2> <p>Scoop's official documentation is very user-friendly for beginners. Instead of elaborating here, it is recommended to read the <a href=https://github.com/ScoopInstaller/Scoop>official documentation</a> or the <a href=https://github.com/ScoopInstaller/Scoop/wiki/Quick-Start>Quick Start guide</a>.</p> <h2 id=qa>Q&amp;A</h2> <h3 id=can-scoop-configure-mirror-sources>Can Scoop Configure Mirror Sources?</h3> <p>The Scoop community only maintains installation configurations, and all software is downloaded from the official download links provided by the software's creators. Therefore, mirror sources are not provided. If your network environment causes repeated download failures, you may need a bit of <a href=../%E7%BF%BB%E5%A2%99/ >magic</a>.</p> <h3 id=why-cant-i-find-java-8>Why Can't I Find Java 8?</h3> <p>For the same reasons mentioned above, the official download links for Java 8 are no longer provided. It is recommended to use <a href=https://github.com/ScoopInstaller/Java/blob/master/bucket/ojdkbuild8.json>ojdkbuild8</a> as a substitute.</p> <h3 id=how-do-i-install-python-2>How Do I Install Python 2?</h3> <p>For software that is outdated and no longer in use, the Scoop community removes it from <a href=https://github.com/ScoopInstaller/Main>ScoopInstaller/Main</a> and adds it to <a href=https://github.com/ScoopInstaller/Versions>ScoopInstaller/Versions</a>. If you need such software, you need to manually add the bucket:</p> <pre><code class=language-powershell>scoop bucket add versions
 scoop install python27
-</code></pre> <aside class=md-source-file> <span class=md-source-file__fact> <span class=md-icon title="Last update"> <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">September 1, 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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@@ -8,7 +8,7 @@
 .\install.ps1 -ScoopDir 'C:\Scoop'
 </code></pre> <h2 id=scoop_3>使用 Scoop</h2> <p>Scoop 的官方文档对于新手非常友好，相对于在此处赘述更推荐阅读 <a href=https://github.com/ScoopInstaller/Scoop>官方文档</a> 或 <a href=https://github.com/ScoopInstaller/Scoop/wiki/Quick-Start>快速入门</a> 。</p> <h2 id=qa>Q&amp;A</h2> <h3 id=scoop_4>Scoop 能配置镜像源吗？</h3> <p>Scoop 社区仅维护安装配置，所有的软件都是从该软件官方提供的下载链接进行下载，所以无法提供镜像源。如果因为你的网络环境导致多次下载失败，那么你需要一点点 <a href=../%E7%BF%BB%E5%A2%99/ >魔法</a>。</p> <h3 id=java8>为什么找不到 Java8？</h3> <p>原因同上，官方已不再提供 Java8 的下载链接，推荐使用 <a href=https://github.com/ScoopInstaller/Java/blob/master/bucket/ojdkbuild8.json>ojdkbuild8</a> 替代。</p> <h3 id=python2>我需要安装 python2 该如何操作？</h3> <p>对于已经过时弃用的软件，Scoop 社区会将其从 <a href=https://github.com/ScoopInstaller/Main>ScoopInstaller/Main</a> 中移除并将其添加到 <a href=https://github.com/ScoopInstaller/Versions>ScoopInstaller/Versions</a> 中。如果你需要这些软件的话需要手动添加 bucket：</p> <pre><code class=language-powershell>scoop bucket add versions
 scoop install python27
-</code></pre> <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">September 1, 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>
+</code></pre> <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> 2023-12-14 </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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@@ -4,7 +4,7 @@
     （3）学习这门课程的体验与感受
     （4）自学这门课的注意点（踩过的坑、难度预警等等）
     （5）... ...
---> <p>深度学习的快速发展和广泛使用很大程度上得益于一系列简单好用且强大的编程框架，例如 Pytorch 和 Tensorflow 等等。但大多数从业者只是这些框架的“调包侠”，对于这些框架内部的细节实现却了解甚少。如果你希望从事深度学习底层框架的开发，或者只是像我一样好奇这些框架的内部实现，那么这门课将会是一个很好的起点。</p> <p>课程的内容大纲覆盖了深度学习系统“全栈”的知识体系。从现代深度学习系统框架的顶层设计，到自微分算法的原理和实现，再到底层硬件加速和实际生产部署。为了更好地掌握理论知识，学生将会在5个课程作业中从头开始设计和实现一个完整的深度学习库 Needle，使其能对计算图进行自动微分，能在 GPU 上实现硬件加速，并且支持各类损失函数、数据加载器和优化器。在此基础上，学生将实现几类常见的神经网络，包括 CNN，RNN，LSTM，Transformer 等等。</p> <p>即使你是深度学习领域的小白也不必过于担心，课程将会循序渐进地从简单分类问题和反向传播优化讲起，一些相对复杂的神经网络都会有配套的 jupyter notebook 详细地描述实现细节。如果你有一定的相关基础知识，那么在学习完自微分部分的内容之后便可以直接上手课程作业，难度虽然不大但相信一定会给你带来新的理解。</p> <p>这门课两位授课教师 <a href=https://zicokolter.com/ >Zico Kolter</a> 和 <a href=https://tqchen.com/ >Tianqi Chen</a> 将所有课程内容都发布了对应的开源版本，但在线评测账号和课程论坛的注册时间已经结束，只剩下框架代码里的本地测试供大家调试代码。或许可以期待明年秋季学期的课程还会发布相应的在线版本供大家学习。</p> <h2 id=_2>课程资源</h2> <ul> <li>课程网站：<a href=https://dlsyscourse.org>https://dlsyscourse.org</a></li> <li>课程视频：<a href="https://www.youtube.com/watch?v=qbJqOFMyIwg">https://www.youtube.com/watch?v=qbJqOFMyIwg</a></li> <li>课程作业：<a href=https://dlsyscourse.org/assignments/ >https://dlsyscourse.org/assignments/</a></li> </ul> <h2 id=_3>资源汇总</h2> <p>@PKUFlyingPig 在学习这门课中用到的所有资源和作业实现都汇总在 <a href=https://github.com/PKUFlyingPig/CMU10-714>PKUFlyingPig/CMU10-714 - GitHub</a> 中。</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">September 1, 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>
+--> <p>深度学习的快速发展和广泛使用很大程度上得益于一系列简单好用且强大的编程框架，例如 Pytorch 和 Tensorflow 等等。但大多数从业者只是这些框架的“调包侠”，对于这些框架内部的细节实现却了解甚少。如果你希望从事深度学习底层框架的开发，或者只是像我一样好奇这些框架的内部实现，那么这门课将会是一个很好的起点。</p> <p>课程的内容大纲覆盖了深度学习系统“全栈”的知识体系。从现代深度学习系统框架的顶层设计，到自微分算法的原理和实现，再到底层硬件加速和实际生产部署。为了更好地掌握理论知识，学生将会在5个课程作业中从头开始设计和实现一个完整的深度学习库 Needle，使其能对计算图进行自动微分，能在 GPU 上实现硬件加速，并且支持各类损失函数、数据加载器和优化器。在此基础上，学生将实现几类常见的神经网络，包括 CNN，RNN，LSTM，Transformer 等等。</p> <p>即使你是深度学习领域的小白也不必过于担心，课程将会循序渐进地从简单分类问题和反向传播优化讲起，一些相对复杂的神经网络都会有配套的 jupyter notebook 详细地描述实现细节。如果你有一定的相关基础知识，那么在学习完自微分部分的内容之后便可以直接上手课程作业，难度虽然不大但相信一定会给你带来新的理解。</p> <p>这门课两位授课教师 <a href=https://zicokolter.com/ >Zico Kolter</a> 和 <a href=https://tqchen.com/ >Tianqi Chen</a> 将所有课程内容都发布了对应的开源版本，但在线评测账号和课程论坛的注册时间已经结束，只剩下框架代码里的本地测试供大家调试代码。或许可以期待明年秋季学期的课程还会发布相应的在线版本供大家学习。</p> <h2 id=_2>课程资源</h2> <ul> <li>课程网站：<a href=https://dlsyscourse.org>https://dlsyscourse.org</a></li> <li>课程视频：<a href="https://www.youtube.com/watch?v=qbJqOFMyIwg">https://www.youtube.com/watch?v=qbJqOFMyIwg</a></li> <li>课程作业：<a href=https://dlsyscourse.org/assignments/ >https://dlsyscourse.org/assignments/</a></li> </ul> <h2 id=_3>资源汇总</h2> <p>@PKUFlyingPig 在学习这门课中用到的所有资源和作业实现都汇总在 <a href=https://github.com/PKUFlyingPig/CMU10-714>PKUFlyingPig/CMU10-714 - GitHub</a> 中。</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> 2023-01-07 </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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diff --git a/机器学习系统/MLC/index.html b/机器学习系统/MLC/index.html
@@ -4,7 +4,7 @@
     （3）学习这门课程的体验与感受
     （4）自学这门课的注意点（踩过的坑、难度预警等等）
     （5）... ...
---> <p>这门课是机器学习编译领域的顶尖学者陈天奇在2022年暑期开设的一门在线课程。其实机器学习编译无论在工业界还是学术界仍然是一个非常前沿且快速更迭的领域，国内外此前还没有为这个方向专门开设的相关课程。因此如果对机器学习编译感兴趣想有个全貌性的感知的话，可以学习一下这门课。</p> <p>本课程主要以 <a href=https://tvm.apache.org/ >Apache TVM</a> 这一主流的机器学习编译框架为例（陈天奇是这个框架的创始人之一），聚焦于如何将开发模式下（如 Tensorflow, Pytorch, Jax）的各类机器学习模型，通过一套普适的抽象和优化算法，变换为拥有更高性能并且适配各类底层硬件的部署模式。课程讲授的知识点都是相对 High-Level 的宏观概念，同时每节课都会有一个配套的 Jupyter Notebook 来通过具体的代码讲解知识点，因此如果从事 TVM 相关的编程开发的话，这门课有丰富且规范的代码示例以供参考。</p> <p>所有的课程资源全部开源并且有中文和英文两个版本，B站和油管分别有中文和英文的课程录影。</p> <h2 id=_2>课程资源</h2> <ul> <li>课程网站：<a href=https://mlc.ai/summer22-zh/ >https://mlc.ai/summer22-zh/</a></li> <li>课程视频：<a href="https://www.bilibili.com/video/BV15v4y1g7EU?spm_id_from=333.337.search-card.all.click&vd_source=a4d76d1247665a7e7bec15d15fd12349">Bilibili</a></li> <li>课程笔记：<a href=https://mlc.ai/zh/index.html>https://mlc.ai/zh/index.html</a></li> <li>课程作业：<a href=https://github.com/mlc-ai/notebooks/blob/main/assignment>https://github.com/mlc-ai/notebooks/blob/main/assignment</a></li> </ul> <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">September 1, 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>
+--> <p>这门课是机器学习编译领域的顶尖学者陈天奇在2022年暑期开设的一门在线课程。其实机器学习编译无论在工业界还是学术界仍然是一个非常前沿且快速更迭的领域，国内外此前还没有为这个方向专门开设的相关课程。因此如果对机器学习编译感兴趣想有个全貌性的感知的话，可以学习一下这门课。</p> <p>本课程主要以 <a href=https://tvm.apache.org/ >Apache TVM</a> 这一主流的机器学习编译框架为例（陈天奇是这个框架的创始人之一），聚焦于如何将开发模式下（如 Tensorflow, Pytorch, Jax）的各类机器学习模型，通过一套普适的抽象和优化算法，变换为拥有更高性能并且适配各类底层硬件的部署模式。课程讲授的知识点都是相对 High-Level 的宏观概念，同时每节课都会有一个配套的 Jupyter Notebook 来通过具体的代码讲解知识点，因此如果从事 TVM 相关的编程开发的话，这门课有丰富且规范的代码示例以供参考。</p> <p>所有的课程资源全部开源并且有中文和英文两个版本，B站和油管分别有中文和英文的课程录影。</p> <h2 id=_2>课程资源</h2> <ul> <li>课程网站：<a href=https://mlc.ai/summer22-zh/ >https://mlc.ai/summer22-zh/</a></li> <li>课程视频：<a href="https://www.bilibili.com/video/BV15v4y1g7EU?spm_id_from=333.337.search-card.all.click&vd_source=a4d76d1247665a7e7bec15d15fd12349">Bilibili</a></li> <li>课程笔记：<a href=https://mlc.ai/zh/index.html>https://mlc.ai/zh/index.html</a></li> <li>课程作业：<a href=https://github.com/mlc-ai/notebooks/blob/main/assignment>https://github.com/mlc-ai/notebooks/blob/main/assignment</a></li> </ul> <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> 2022-09-06 </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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