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<div id='write' class = 'is-node'><h1><a name='header-n2' class='md-header-anchor '></a>神经网络与深度学习</h1><p>作者：<a href='https://xpqiu.github.io/'>邱锡鹏</a> 微博：<a href='http://weibo.com/xpqiu'>@邱锡鹏</a></p><h2><a name='header-n4' class='md-header-anchor '></a>关于本书</h2><p>近年来，以机器学习、知识图谱为代表的人工智能技术逐渐变得普及。从车牌识别、人脸识别、语音识别、智能问答、推荐系统到自动驾驶，人们在日常生活中都可能有意无意地使用到了人工智能技术。这些技术的背后都离不开人工智能领域研究者们的长期努力。特别是最近这几年，得益于数据的增多、计算能力的增强、学习算法的成熟以及应用场景的丰富，越来越多的人开始关注这一个“崭新”的研究领域：<em>深度学习</em>。深度学习以神经网络为主要模型，一开始用来解决机器学习中的表示学习问题。但是由于其强大的能力，深度学习越来越多地用来解决一些通用人工智能问题，比如推理、决策等。目前，深度学习技术在学术界和工业界取得了广泛的成功，受到高度重视，并掀起新一轮的人工智能热潮。</p><p>本课程主要介绍神经网络与深度学习中的基础知识、主要模型（卷积神经网络、循环神经网络等）以及在计算机视觉、自然语言处理等领域的应用。</p><p>要获取更新提醒，请关注<a href='https://github.com/nndl/nndl.github.io' target='_blank' class='url'>https://github.com/nndl/nndl.github.io</a></p><p>示例代码，见<a href='https://github.com/nndl/nndl-codes' target='_blank' class='url'>https://github.com/nndl/nndl-codes</a></p><p>课程练习，见<a href='https://github.com/nndl/exercise' target='_blank' class='url'>https://github.com/nndl/exercise</a></p><h2><a name='header-n10' class='md-header-anchor '></a>概要</h2><p>《神经网络与深度学习》3小时课程概要 <a href='./ppt/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E4%B8%8E%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0-3%E5%B0%8F%E6%97%B6.pptx'>ppt</a>(72M) <a href='./ppt/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E4%B8%8E%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0-3%E5%B0%8F%E6%97%B6.pdf'>pdf</a> (12M) </p><p>全书内容 <a href='nndl-book.pdf'>pdf</a> (updated 2019-4-10)、<a href='nndl-book-ipad.pdf'>ipad版</a> (updated 2019-4-10)</p><h2><a name='header-n13' class='md-header-anchor '></a>章节内容</h2><ol start='' ><li><a href='chap-%E7%BB%AA%E8%AE%BA.pdf'>绪论</a>[<a href='./ppt/chap-%E7%BB%AA%E8%AE%BA.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.pdf'>机器学习概述</a> [<a href='./ppt/chap-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E7%BA%BF%E6%80%A7%E6%A8%A1%E5%9E%8B.pdf'>线性模型</a> [<a href='./ppt/chap-%E7%BA%BF%E6%80%A7%E6%A8%A1%E5%9E%8B.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E5%89%8D%E9%A6%88%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.pdf'>前馈神经网络</a> [<a href='./ppt/chap-%E5%89%8D%E9%A6%88%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.pdf'>卷积神经网络</a> [<a href='./ppt/chap-%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.pdf'>循环神经网络</a> [<a href='./ppt/chap-%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E7%BD%91%E7%BB%9C%E4%BC%98%E5%8C%96%E4%B8%8E%E6%AD%A3%E5%88%99%E5%8C%96.pdf'>网络优化与正则化</a> [<a href='./ppt/chap-%E7%BD%91%E7%BB%9C%E4%BC%98%E5%8C%96%E4%B8%8E%E6%AD%A3%E5%88%99%E5%8C%96.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6%E4%B8%8E%E5%A4%96%E9%83%A8%E8%AE%B0%E5%BF%86.pdf'>注意力机制与外部记忆</a> [<a href='./ppt/chap-%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6%E4%B8%8E%E5%A4%96%E9%83%A8%E8%AE%B0%E5%BF%86.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0.pdf'>无监督学习</a> [<a href='./ppt/chap-%E6%97%A0%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E6%A8%A1%E5%9E%8B%E7%8B%AC%E7%AB%8B%E7%9A%84%E5%AD%A6%E4%B9%A0%E6%96%B9%E5%BC%8F.pdf'>模型独立的学习方式</a> (updated 2019-4-4)</li><li><a href='chap-%E6%A6%82%E7%8E%87%E5%9B%BE%E6%A8%A1%E5%9E%8B.pdf'>概率图模型</a> [<a href='./ppt/chap-%E6%A6%82%E7%8E%87%E5%9B%BE%E6%A8%A1%E5%9E%8B.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E6%B7%B1%E5%BA%A6%E4%BF%A1%E5%BF%B5%E7%BD%91%E7%BB%9C.pdf'>深度信念网络</a> [<a href='./ppt/chap-%E6%B7%B1%E5%BA%A6%E4%BF%A1%E5%BF%B5%E7%BD%91%E7%BB%9C.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E6%B7%B1%E5%BA%A6%E7%94%9F%E6%88%90%E6%A8%A1%E5%9E%8B.pdf'>深度生成模型</a>[<a href='./ppt/chap-%E6%B7%B1%E5%BA%A6%E7%94%9F%E6%88%90%E6%A8%A1%E5%9E%8B.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E6%B7%B1%E5%BA%A6%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0.pdf'>深度强化学习</a> [<a href='./ppt/chap-%E6%B7%B1%E5%BA%A6%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0.pptx'>ppt</a>] (updated 2019-4-4)</li><li><a href='chap-%E5%BA%8F%E5%88%97%E7%94%9F%E6%88%90%E6%A8%A1%E5%9E%8B.pdf'>序列生成模型</a> (updated 2019-4-4) 一个过时版本：<a href='chap-%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E4%B8%8E%E8%AF%8D%E5%B5%8C%E5%85%A5.pdf'>词嵌入与语言模型</a></li><li><a href='chap-%E6%95%B0%E5%AD%A6%E5%9F%BA%E7%A1%80.pdf'>数学基础</a> (updated 2019-4-4)</li></ol><h2><a name='header-n48' class='md-header-anchor '></a>反馈意见</h2><p>如果您有任何意见、评论以及建议（先确认最新版本中是否已经修正），请通过GitHub的<a href='https://github.com/nndl/nndl.github.io/issues'>Issues</a>页面进行反馈。如果错误比较重要，我会在本书中进行致谢。</p><p>反馈意见包括但不限于：（因为分开排版关系，页码错误请忽略。）</p><ul><li>打字错误</li><li>描述错误: 比如“感知器是非线性分类器”</li><li>评论</li><li>建议</li></ul><p>非常感谢！</p><p>致谢列表：感谢王利锋、张钧瑞、李浩、胡可鑫、韦鹏辉、徐国海、侯宇蓬、任强、王少敬等同学指出书中的错误。</p><p> </p><h2><a name='header-n63' class='md-header-anchor '></a>推荐课程</h2><h4><a name='header-n64' class='md-header-anchor '></a>Stat212b：Topics Course on Deep Learning</h4><p><a href='http://joanbruna.github.io/stat212b/'>http://joanbruna.github.io/stat212b/</a></p><p>加州大学伯克利分校统计系Joan Bruna（Yann LeCun博士后）
以统计的角度讲解DL。</p><h4><a name='header-n67' class='md-header-anchor '></a>CS224d: Deep Learning for Natural Language Processing</h4><p><a href='http://cs224d.stanford.edu/'>http://cs224d.stanford.edu/</a></p><p>斯坦福大学 Richard Socher
主要讲解自然语言处理领域的各种深度学习模型</p><h4><a name='header-n70' class='md-header-anchor '></a>CS231n:Convolutional Neural Networks for Visual Recognition</h4><p><a href='http://cs231n.stanford.edu/'>http://cs231n.stanford.edu/</a></p><p>斯坦福大学 Fei-Fei Li Andrej Karpathy
主要讲解CNN、RNN在图像领域的应用</p><p> </p><h2><a name='header-n74' class='md-header-anchor '></a>致谢</h2><p>本页面由<a href='http://www.typora.io/'>Typora</a>编辑。</p><p> </p></div>
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