nykek5i 发表于 2024-9-28 12:25:32

AI初学者必看：第 3 篇 - 深度学习基本

<img src="https://mmbiz.qpic.cn/sz_mmbiz_jpg/Ft50DicML6X9If2U0GYNaoBPYNmezG5ib8qTmp3NhnTsuVZBgJPHFsqEYVfKEnwdTVhlyUxuicddicxicvv7YsnqhTA/640?wx_fmt=jpeg&amp;from=appmsg&amp;tp=webp&amp;wxfrom=5&amp;wx_lazy=1&amp;wx_co=1" style="width: 50%; margin-bottom: 20px;">
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;"><strong style="color: blue;"><span style="color: black;">介绍</span></strong></span></p><span style="color: black;">“人工智能（AI）”一词于 1956 年<span style="color: black;">面世</span>，如今已为<span style="color: black;">大众</span>所熟知。然而，在 ChatGPT <span style="color: black;">快速</span>流行之前，AI 的<span style="color: black;">运用</span>和讨论大多局限于科学<span style="color: black;">科研</span>或虚构电影。如今，AI 尤其是生成式 AI 已<span style="color: black;">作为</span><span style="color: black;">大众</span><span style="color: black;">热榜</span>的<span style="color: black;">专题</span>。</span><span style="color: black;">初学者生成式人工智能科普系列内容面向<span style="color: black;">期盼</span>踏上人工智能之旅的任何人。<span style="color: black;">日前</span>，无论您的角色和工作内容<span style="color: black;">怎样</span>，无论您是技术人员还是<span style="color: black;">制品</span>专家，<span style="color: black;">或</span>担任其他任何角色，学习<span style="color: black;">认识</span>生成式人工智能的<span style="color: black;">基本</span>知识绝对是一个明智之举。</span><span style="color: black;">在本系列<span style="color: black;">文案</span>中，<span style="color: black;">咱们</span>将一步步分享生成式人工智能的<span style="color: black;">基本</span>知识。为了便于理解，将<span style="color: black;">全部</span>系列分为8篇内容（阅读时间15~20分钟）：</span>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><a style="color: black;"><span style="color: black;">第 1 篇--人工智能简介</span></a><span style="color: black;">（点击查看）</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><a style="color: black;"><span style="color: black;">第 2 篇--理解<span style="color: black;">设备</span>学</span></a>习<span style="color: black;">（点击查看）</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;"><strong style="color: blue;"><span style="color: black;">第 3 篇--深度学习<span style="color: black;">基本</span><strong style="color: blue;"><span style="color: black;">[当前内容]</span></strong></span></strong></span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">第 4 篇--生成式人工智能简介</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">第 5 篇--什么是大型语言模型 (LLM)？</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">第 6 篇--与人工智能沟通的艺术</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">第 7 篇--生成式人工智能中的伦理考量</span></p><span style="color: black;">第 8 篇--生成式人工智能的挑战和局限性</span>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">下面是本系列第 3 篇内容——探讨深度学习技术<span style="color: black;">基本</span></span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">什么是深度学习？</span></strong></p><span style="color: black;"><span style="color: black;">设备</span>能像<span style="color: black;">咱们</span>人类（人脑）学习事物的方式<span style="color: black;">同样</span>学习吗？——这<span style="color: black;">便是</span>深度学习创新<span style="color: black;">背面</span>的想法。</span><strong style="color: blue;"><span style="color: black;">深度学习是<span style="color: black;">设备</span>学习的一个子集</span></strong><span style="color: black;">（ML 又是 AI 的一个子集）。深度学习的核心是基于<strong style="color: blue;">人工神经网络 (ANN)</strong>，这是一种受人类大脑结构和功能启发的计算模型。</span>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="https://mmbiz.qpic.cn/sz_mmbiz_jpg/Ft50DicML6X9QrFwXmvCLHv0RknH9TjxdHjD1LzmDkH8hRzVxSPdiczhX7SgA6YyawpUUllCibzxtrZ55dNDee1Ug/640?wx_fmt=jpeg&amp;tp=webp&amp;wxfrom=5&amp;wx_lazy=1&amp;wx_co=1" style="width: 50%; margin-bottom: 20px;"></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">听起来有点令人困惑？让<span style="color: black;">咱们</span>用通俗易懂的语言来简化它！</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;"><span style="color: black;">首要</span>，让<span style="color: black;">咱们</span><span style="color: black;">认识</span>几个重要的概念。</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">人脑中的生物神经网络</span></strong></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">神经元是人脑最基本的细胞。人脑有数十亿个神经元，它们相互<span style="color: black;">功效</span>、沟通，形成神经网络。</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="https://mmbiz.qpic.cn/sz_mmbiz_png/Ft50DicML6X9QrFwXmvCLHv0RknH9TjxdntPHKvnXhSwBbaOFzdvI06TbrPf4wSOloQsSzZlyYAC0wrnZ4icRicLg/640?wx_fmt=png&amp;from=appmsg&amp;tp=webp&amp;wxfrom=5&amp;wx_lazy=1&amp;wx_co=1" style="width: 50%; margin-bottom: 20px;"></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">这些神经元接收许多输入，从<span style="color: black;">咱们</span>看到和听到的事物到<span style="color: black;">咱们</span>感受到的一切，<span style="color: black;">而后</span>向其他神经元发送信息，这些神经元依次做出反应。工作神经网络使人类能够思考，更重要的是，能够学习。</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">人工神经网络（ANN）</span></strong></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">人工神经网络是基于人脑生物神经网络设计的计算网络。</span></strong></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">人类大脑中有相互连接的神经元。<span style="color: black;">一样</span>，人工神经网络中<span style="color: black;">亦</span>有相互连接的神经元。这些神经元被<span style="color: black;">叫作</span>为节点。</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">让<span style="color: black;">咱们</span>尝试简化 ANN！</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">想象一下用<span style="color: black;">区别</span>形状和<span style="color: black;">体积</span>的管道制作一个巨大的 3D 结构。每根管道都<span style="color: black;">能够</span>连接到许多其他管道，并且有一个<span style="color: black;">能够</span>打开或关闭的开关。这为您<span style="color: black;">供给</span>了连接管道的多种方式，这看起来有点棘手，对吧？</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;"><span style="color: black;">此刻</span>，让<span style="color: black;">咱们</span>把这个管子连接到水龙头上。<span style="color: black;">区别</span>尺寸的管子让水以<span style="color: black;">区别</span>的速度流动。<span style="color: black;">倘若</span><span style="color: black;">咱们</span>关闭开关，水就不会流动。</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">水<span style="color: black;">表率</span>流经大脑的数据，管道<span style="color: black;">表率</span>大脑中<span style="color: black;">叫作</span>为神经元的部分。</span></strong></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">人工神经网络的架构</span></strong></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">人工神经网络<span style="color: black;">重点</span>由三层<span style="color: black;">构成</span>——<strong style="color: blue;">输入层、输出层和<span style="color: black;">隐匿</span>层。</strong></span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">想象一个类似于三层三明治的人工神经网络。</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;"><span style="color: black;">第1</span>层<span style="color: black;">叫作</span>为<strong style="color: blue;">输入层</strong>，<span style="color: black;">表率</span>面包的底部。它接收信息。</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">第二层<span style="color: black;">叫作</span>为<strong style="color: blue;"><span style="color: black;">隐匿</span>层</strong>，<span style="color: black;">表率</span>中间的美味馅料。它会思考并<span style="color: black;">处理</span>问题。</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">第三层<span style="color: black;">叫作</span>为<strong style="color: blue;">输出层</strong>，<span style="color: black;">表率</span>面包的顶部切片。它为<span style="color: black;">咱们</span><span style="color: black;">供给</span>了<span style="color: black;">最后</span>结果。</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="https://mmbiz.qpic.cn/sz_mmbiz_jpg/Ft50DicML6X9QrFwXmvCLHv0RknH9TjxdVMrcmJpM0CxWuGp2zakzk9WxDExwSPWFliav5bxcT2u90JubEmw21NQ/640?wx_fmt=jpeg&amp;tp=webp&amp;wxfrom=5&amp;wx_lazy=1&amp;wx_co=1" style="width: 50%; margin-bottom: 20px;"></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">简而言之：</span></p><strong style="color: blue;"><span style="color: black;">输入层</span></strong><span style="color: black;">这<span style="color: black;">便是</span>信息进入人工神经网络的<span style="color: black;">地区</span></span>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">它是起点，网络从<span style="color: black;">这儿</span>接收其需要处理的数据</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">输出层</span></strong></p><span style="color: black;">这是网络给出<span style="color: black;">最后</span>结果或答案的<span style="color: black;">地区</span></span>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">它是终点，网络在<span style="color: black;">这儿</span>告诉<span style="color: black;">咱们</span>它学到了什么或决定了什么</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;"><span style="color: black;">隐匿</span>层</span></strong></p><span style="color: black;">这些层<span style="color: black;">位置于</span>输入层和输出层之间</span>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">这些层中的神经元处理信息并<span style="color: black;">帮忙</span>网络学习模式和做出决策</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">人工神经网络<span style="color: black;">怎样</span>工作？</span></strong></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">想象一下一群<span style="color: black;">孩儿</span>试图<span style="color: black;">经过</span>分享<span style="color: black;">她们</span>的观察来识别一只熊猫。</span></p><span style="color: black;"><span style="color: black;">每一个</span><span style="color: black;">孩儿</span>都<span style="color: black;">拥有</span>特定的特征，例如黑白色的皮毛、圆圆的脸和独特的眼睛</span><span style="color: black;">就个人而言，<span style="color: black;">她们</span>可能<span style="color: black;">没法</span>完全理解熊猫的样子</span>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">但<span style="color: black;">经过</span>结合<span style="color: black;">她们</span>的见解，<span style="color: black;">她们</span>形<span style="color: black;">成为了</span>一种集体理解</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">在人工神经网络的世界里，<strong style="color: blue;">这些<span style="color: black;">孩儿</span><span style="color: black;">表率</span>着神经元</strong>。</span></p><span style="color: black;">在人工神经网络中，单个“神经元”（类似于<span style="color: black;">咱们</span>例子中的<span style="color: black;">孩儿</span>）专门识别特定方面。</span><span style="color: black;">结合起来，它们有助于识别整体概念（熊猫）。</span>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">网络<span style="color: black;">经过</span>反复接触来完善其理解，类似于孩子们随着时间的推移完善<span style="color: black;">她们</span>的熊猫识别技能。</span></p><img src="data:image/svg+xml,%3C%3Fxml version=1.0 encoding=UTF-8%3F%3E%3Csvg width=1px height=1px viewBox=0 0 1 1 version=1.1 xmlns=http://www.w3.org/2000/svg xmlns:xlink=http://www.w3.org/1999/xlink%3E%3Ctitle%3E%3C/title%3E%3Cg stroke=none stroke-width=1 fill=none fill-rule=evenodd fill-opacity=0%3E%3Cg transform=translate(-249.000000, -126.000000) fill=%23FFFFFF%3E%3Crect x=249 y=126 width=1 height=1%3E%3C/rect%3E%3C/g%3E%3C/g%3E%3C/svg%3E" style="width: 50%; margin-bottom: 20px;">
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">输入层（观察）：</span></strong></p><span style="color: black;"><span style="color: black;">每一个</span><span style="color: black;">孩儿</span>都会观察一个方面，例如毛皮颜色或脸型，形成<span style="color: black;">咱们</span>网络的输入层。</span><strong style="color: blue;"><span style="color: black;"><span style="color: black;">隐匿</span>层（处理）：</span></strong><span style="color: black;"><span style="color: black;">孩儿</span>们互相传递观察结果，模仿神经网络的<span style="color: black;">隐匿</span>层。在分享信息的过程中，<span style="color: black;">她们</span><span style="color: black;">一起</span>对熊猫的特征有了更全面的<span style="color: black;">认识</span>。</span><strong style="color: blue;"><span style="color: black;">输出层（识别）：</span></strong><span style="color: black;">最后，<span style="color: black;">她们</span>综合所有细节得出结论。<span style="color: black;">倘若</span>大<span style="color: black;">都数</span>人同意观察到的特征与熊猫相符，<span style="color: black;">她们</span>就会输出“熊猫”。这个输出层对应于网络的<span style="color: black;">最后</span>决策。</span><strong style="color: blue;"><span style="color: black;">评分<span style="color: black;">办法</span>：</span></strong><span style="color: black;">为了<span style="color: black;">加强</span>识别能力，<span style="color: black;">孩儿</span>们会跟踪其准确度。</span><span style="color: black;"><span style="color: black;">倘若</span><span style="color: black;">她们</span>正确识别了熊猫，<span style="color: black;">她们</span>就会<span style="color: black;">得到</span>积分；</span><span style="color: black;">否则，<span style="color: black;">她们</span>就会从错误中吸取教训。</span><span style="color: black;">类似地，在神经网络中，评分<span style="color: black;">办法</span>有助于<span style="color: black;">调节</span>网络的参数，以<span style="color: black;">加强</span>随着时间的推移的准确性。</span><span style="color: black;">该团队合作展示了人工神经网络<span style="color: black;">怎样</span>逐层处理信息，从<span style="color: black;">各样</span>特征中学习并<span style="color: black;">经过</span>评分机制<span style="color: black;">持续</span>完善其理解。</span><strong style="color: blue;"><span style="color: black;">深度神经网络</span></strong><span style="color: black;">深度神经网络 (DNN) 是一种在输入层和输出层之间<span style="color: black;">拥有</span>多层的人工神经网络 (ANN) 。</span><span style="color: black;"><span style="color: black;">这儿</span>的<strong style="color: blue;">“深度”</strong><span style="color: black;">寓意</span>着输入和输出之间有多层，使其能够学习<span style="color: black;">繁杂</span>的模式。</span><strong style="color: blue;"><span style="color: black;">关于深度学习的要点</span></strong><span style="color: black;"><span style="color: black;">此刻</span>，<span style="color: black;">咱们</span>来总结一下深度学习的<span style="color: black;">有些</span>要点！</span>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">1-<span style="color: black;">设备</span>学习子集</span></strong></p><span style="color: black;">深度学习是<span style="color: black;">设备</span>学习的子集，而<span style="color: black;">设备</span>学习又是人工智能的子集。</span><strong style="color: blue;"><span style="color: black;">2-受大脑启发</span></strong><span style="color: black;">深度学习基于人工神经网络，其灵感来自于<span style="color: black;">咱们</span>大脑的工作方式。</span><strong style="color: blue;"><span style="color: black;">3-人工神经网络（ANN）</span></strong><span style="color: black;">ANN 是一种模拟人脑生物神经网络的计算网络。</span><strong style="color: blue;"><span style="color: black;">4-深度神经网络</span></strong><span style="color: black;">形容词“深度”<span style="color: black;">指的是</span>网络中<span style="color: black;">运用</span>多层。它<span style="color: black;">运用</span><span style="color: black;">拥有</span>多个<span style="color: black;">隐匿</span>层的深度神经网络。</span><span style="color: black;">这些层处理信息，使系统能够学习<span style="color: black;">繁杂</span>的模式。</span><strong style="color: blue;"><span style="color: black;">5-从数据中学习</span></strong><span style="color: black;">系统<span style="color: black;">经过</span>展示<span style="color: black;">海量</span>示例并<span style="color: black;">按照</span>预测和正确答案之间的差异<span style="color: black;">调节</span>神经元之间的连接进行学习。</span><strong style="color: blue;"><span style="color: black;">6-处理<span style="color: black;">繁杂</span>问题</span></strong><span style="color: black;">深度学习<span style="color: black;">针对</span><span style="color: black;">处理</span>传统<span style="color: black;">办法</span>难以<span style="color: black;">处理</span>的<span style="color: black;">繁杂</span>问题<span style="color: black;">尤其</span>有效。</span><strong style="color: blue;"><span style="color: black;"><span style="color: black;">设备</span>学习与深度学习</span></strong><span style="color: black;">让<span style="color: black;">咱们</span>分析一下<span style="color: black;">设备</span>学习和深度学习之间的<span style="color: black;">重点</span>区别：</span>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">在<span style="color: black;">咱们</span>之前的文章中，<span style="color: black;">咱们</span>谈到了让<span style="color: black;">设备</span>人识别狗。假设<span style="color: black;">咱们</span>想让<span style="color: black;">设备</span>人识别几种动物。</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="data:image/svg+xml,%3C%3Fxml version=1.0 encoding=UTF-8%3F%3E%3Csvg width=1px height=1px viewBox=0 0 1 1 version=1.1 xmlns=http://www.w3.org/2000/svg xmlns:xlink=http://www.w3.org/1999/xlink%3E%3Ctitle%3E%3C/title%3E%3Cg stroke=none stroke-width=1 fill=none fill-rule=evenodd fill-opacity=0%3E%3Cg transform=translate(-249.000000, -126.000000) fill=%23FFFFFF%3E%3Crect x=249 y=126 width=1 height=1%3E%3C/rect%3E%3C/g%3E%3C/g%3E%3C/svg%3E" style="width: 50%; margin-bottom: 20px;"></p><span style="color: black;"><strong style="color: blue;"><span style="color: black;">结尾</span></strong></span>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">在这篇<span style="color: black;">文案</span>中，<span style="color: black;">咱们</span><span style="color: black;">认识</span>了什么是深度学习以及它是<span style="color: black;">怎样</span>工作的。深度学习因其多层神经网络而得名，它类似于<span style="color: black;">拥有</span>多层思维的人类大脑神经网络，每一层都有助于更深入地理解它所处理的信息。</span></p>
    <p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">从识别图像和理解语音到为语音助手和自动驾驶汽车<span style="color: black;">供给</span>动力，深度学习有助于<span style="color: black;">处理</span>许多<span style="color: black;">繁杂</span>的任务。</span></p><strong style="color: blue;"><span style="color: black;"><span style="color: black;">倘若</span>您有任何疑问或想法，欢迎评论区留言探讨。</span></strong><span style="color: black;"><strong style="color: blue;"><span style="color: black;">下期内容--AI初学者：第 4 部分 - 生成式人工智能简介</span></strong></span>
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b1gc8v 发表于 2024-10-12 14:06:45

感谢您的精彩评论，为我带来了新的思考角度。

j8typz 发表于 2024-10-20 05:46:27

我完全赞同你的观点，思考很有深度。

wrjc1hod 发表于 2024-10-24 05:42:35

你的努力一定会被看见，相信自己，加油。
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