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<meta property="og:description" content="欢迎光临本人的小站~ 这是一个经验分享兼踩坑记录兼资源分享的个人博客, 目前还在不断丰富之中(~ ̄▽ ̄)~咕咕咕 本人目前硕士在读, 学习的内容包括但不限于: 熟练使用 Visual Studio 进行项目开发并输出 &#96;锟斤拷&#96; 和 &#96;烫烫烫&#96;. 熟悉 PyTorch 等深度学习框架的安装. 熟悉爬虫相关技术然后被禁 IP. 在 Github 参与开源项目的 clone. 了解常见密码算法的拼写">
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Expand Up @@ -385,6 +385,7 @@ <h2 class="post-title" itemprop="name headline">

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<p>为了用 Simpleperf 在非 root 机上进行性能分析, 需要在 apk 的清单文件中设置 <code>android:debuggable=&quot;true&quot;</code> 的标记, 因此研究了一下怎么对 apk 进行重打包, 对修改后的包进行性能分析.</p>
<p>本文参考<span class="exturl" data-url="aHR0cHM6Ly9jcmlmYW4uZ2l0aHViLmlvL2FuZHJvaWRfcmVfcmVwYWNrX2Fway93ZWJzaXRlL3JlcGFja19wcm9jZXNzLw==">重新打包apk流程<i class="fa fa-external-link-alt"></i></span>.</p>
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<meta property="og:description" content="这是网安本科速通系列的最后一篇了, 主题还是 pytorch, 但是问题换成了 NLP, 并且还是以经典的文本分类作为示例, 数据集使用与前面经典问题之文本分类相同的一份小数据集, 方便进行比较. 阅读本篇前需要先看前两篇, 经典问题之文本分类与基于 PyTorch 的手写数字分类. 本篇的项目代码和数据集是以前两篇作为基础的, 并且也会精简正文内容.">
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<meta property="og:description" content="PCA 算法, 也就是主成分分析法 (Principal Component Analysis), 是一种数据降维算法, 能够在尽可能保留数据特征的同时压缩数据, 降低数据复杂度, 便于数据分析的进行. 本文简要介绍 PCA 算法的基本思想与数学推导, 并在文末提供对应的 python 实现.">
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<meta property="og:description" content="Windows 10 激活工具存档, 下载地址: W10_Digital_Activation_Program_v1.4.6_Chs.zip. 校验码: 1234MD5: A7A44635E59CC9C403EBA7873D1B6D0ESHA1: 13275066B9D3FBFF8F99AA7CDF89D5AC2E94F571SHA2-256: E44F2F49A0805F3B0B7CB4720B">
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<meta property="og:description" content="IDM 破解版, 下载地址: IDM-v6.41.11-Repack.zip. 校验码: 1234MD5: EC358D55F0B1657A3095568747CC5B27SHA1: 8F544AA6CA37BA181A6D942441CC65C0B448FE42SHA2-256: 8A6DE19A91AE4644762D89C507946AB8E7D096B39D14A32B42C2FAACF9">
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<meta property="og:description" content="Typora v1.3.8 破解版, 下载地址: typora-setup-x64.zip. 安装包安装之后, 将压缩包里的 winmm.dll 文件放到程序安装目录下. 可能会报毒, 需要手动添加信任. 校验码: 1234MD5: 8AA8CCF545DA329B44E56700C9D2F468SHA1: A10C3549E148C7C8124AF9A46D4D3882AC86214BSHA2-">
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