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<a href="/2021/07/27/deep-learning-from-scratch/" class="post-title-link" itemprop="url">딥러닝, 처음 접하는 사람들을 위한 기초</a>
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<time title="Created: 2021-07-27 16:33:59" itemprop="dateCreated datePublished" datetime="2021-07-27T16:33:59+09:00">2021-07-27</time>
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,
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<span class="post-meta-item-text">Symbols count in article: </span>
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<h2 id="들어가며"><a href="#들어가며" class="headerlink" title="들어가며"></a>들어가며</h2><p>올해 상반기, 나의 최대 관심사는 무엇보다도 <strong><code>졸업(!!!)</code></strong>이었다. 많은 대학원생들이 으레 그렇듯 예상치 못한 사정으로 졸업이 예정보다 반년 늦어졌고 그마저도 더 늦어지겠다 싶어 모든 우선 순위를 제쳐두고 학위 논문을 준비하게 되었다.</p>
<p>그렇게 1월 말부터 논문을 구상하고 작성하며 시간이 흘렀고, 실험 과정에서 굉장히 많은 시행 착오도 겪었다. 두 번의 발표와 도장을 받기위해 교수님들께 메일을 보내고 돌아다니며 모든 서류 제출을 끝내고 나니 6월이 되어있었다. 그렇게 학위 논문을 모두 제출하고 대학원과 관련된 모든 것을 털어내고서야 드디어 <strong>석사(진)</strong>이 되었다.</p>
<p>그래도 8월까지는 학생이니만큼 학생 신분의 마지막이 지나기 전에 자축의 의미로 본 포스팅을 작성하기로 하였다. 사실, 2년 반 동안의 지난했던 기간의 회고를 할까도 생각했지만 너무 할 말<del>(못 할 말)</del>이 많아지기 때문에 다소 식상하지만 학위 논문을 작성하면서 정리했던 인공지능의 발전사와 개략적인 개념을 최대한 많은 사람들이 이해할 수 있도록 포스팅에 설명해보기로 했다.</p>
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<a href="/2021/07/06/global-interpreter-lock/" class="post-title-link" itemprop="url">Python GIL(Global Interpreter Lock)이 뭐에요?</a>
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<span>6 mins.</span>
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<h2 id="들어가며"><a href="#들어가며" class="headerlink" title="들어가며"></a>들어가며</h2><p>딥러닝을 입문하며 자연스럽게 파이썬을 함께 공부하게 되었고, 지금은 주 언어로 사용하고 있다. 하지만 나는 파이썬에 대해 얼마나 알고 있을까?</p>
<p>C/C++ 같은 컴파일 언어만 사용했던 나에게 파이썬은 꽤 난해하게 다가왔다. 사실, 파이썬을 사용하면서 느낀 생각은 “파이썬의 철학(The Zen of Python)과 많이 다른데..?” 였다.</p>
<p>파이썬 철학의 핵심 내용은 <code>명확한 그리고 가급적이면 유일한 하나의 방법이 존재한다(There should be one-- and preferably only one --obvious way to do it)</code>이며, 이에 따르면 규칙이 굉장히 엄격한 언어라고 한다. 그렇지만 나에게 파이썬의 처음에는 굉장히 자유로운 언어로 보였다.</p>
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<a href="/2020/07/14/retrospective-to-overcome-the-burnout/" class="post-title-link" itemprop="url">번아웃 극복을 위한 복기</a>
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<span>3 mins.</span>
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<p>바둑에 대해서 잘은 모르지만 그로부터 유래된 단어는 간혹 일상에서 사용하곤 한다.<br>바둑 용어 중에 <code>복기</code>라는 말이 있다.<br>대국이 끝나고 승패가 결정되었음에도 내용을 검토하기 위해 상호 간 처음부터 두었던 순서대로 다시 두어 재연함 뜻하는 단어다.<br>복기로부터 바둑 기사들은 대국에서 잘한 수와 잘못했던 수, 상대방의 전략 등을 살펴본다.<br>프로 9단 바둑기사 이창호는 다음과 같이 말할 정도로 복기를 중요시 했다고 한다.</p>
<blockquote>
<p><strong><em>승리한 대국의 복기는 이기는 습관을 만들어주고, 패배한 대국의 복기는 이기는 준비를 만들어준다</em></strong></p>
</blockquote>
<p>조훈현 9단 또한 복기가 가지않았던 길을 갈 수 있게 한다 하였으며, 패배로 인해 아플수록 더 예민하게 들여다보고 복기해야한다는 말을 했더랬다.<br>이 바둑기사들이 많은 수를 순서대로 기억하여 복기를 할 수 있는 이유는 한 수 한 수에 집중하고 의미를 부여하여 두었기 때문이라고 한다.</p>
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<span>7 mins.</span>
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<blockquote>
<p><strong><em>Migration to <span class="exturl" data-url="aHR0cHM6Ly9qaW1pbnRoZWJveC5ub3Rpb24uc2l0ZS9NZXRhLVNoZWV0LTA0ODJlM2U1MGExNDQ3MTBiMjUzMzE2ODlkMTg3ZTE2Lw==">Meta Sheet<i class="fa fa-external-link-alt"></i></span></em></strong></p>
</blockquote>
<hr>
<h2 id="U-Net-Convolutional-Networks-for-Biomedical-Image-Segmentation"><a href="#U-Net-Convolutional-Networks-for-Biomedical-Image-Segmentation" class="headerlink" title="U-Net: Convolutional Networks for Biomedical Image Segmentation"></a>U-Net: Convolutional Networks for Biomedical Image Segmentation</h2><p>MICCAI 2015</p>
<hr>
<h3 id="Paper"><a href="#Paper" class="headerlink" title="Paper"></a>Paper</h3><ul>
<li><span class="exturl" data-url="aHR0cHM6Ly9hcnhpdi5vcmcvcGRmLzE1MDUuMDQ1OTcucGRm">https://arxiv.org/pdf/1505.04597.pdf<i class="fa fa-external-link-alt"></i></span></li>
</ul>
<h3 id="Source-Code-Repository"><a href="#Source-Code-Repository" class="headerlink" title="Source Code Repository"></a>Source Code Repository</h3><ul>
<li>Official Model: <span class="exturl" data-url="aHR0cHM6Ly9sbWIuaW5mb3JtYXRpay51bmktZnJlaWJ1cmcuZGUvcmVzb3VyY2VzL29wZW5zb3VyY2UvdW5ldC8=">https://lmb.informatik.uni-freiburg.de/resources/opensource/unet/<i class="fa fa-external-link-alt"></i></span><ul>
<li>Framework: Caffe</li>
</ul>
</li>
<li>Reproduce Model: <span class="exturl" data-url="aHR0cHM6Ly9naXRodWIuY29tL2pha2VyZXQvdGZfdW5ldA==">https://github.com/jakeret/tf_unet<i class="fa fa-external-link-alt"></i></span><ul>
<li>Framework: Tensorflow</li>
<li>Paper: <span class="exturl" data-url="aHR0cHM6Ly9hcnhpdi5vcmcvcGRmLzE2MDkuMDkwNzcucGRm">https://arxiv.org/pdf/1609.09077.pdf<i class="fa fa-external-link-alt"></i></span> in Astronomy and Computing 2017</li>
<li>Documentation: <span class="exturl" data-url="aHR0cHM6Ly90Zi11bmV0LnJlYWR0aGVkb2NzLmlvL2VuL2xhdGVzdC9pbnN0YWxsYXRpb24uaHRtbA==">https://tf-unet.readthedocs.io/en/latest/installation.html<i class="fa fa-external-link-alt"></i></span></li>
</ul>
</li>
<li>Reproduce Model: <span class="exturl" data-url="aHR0cHM6Ly9naXRodWIuY29tL3poaXh1aGFvL3VuZXQ=">https://github.com/zhixuhao/unet<i class="fa fa-external-link-alt"></i></span><ul>
<li>Framework: Keras</li>
</ul>
</li>
<li>Reproduce Model: <span class="exturl" data-url="aHR0cHM6Ly9naXRodWIuY29tL21pbGVzaWFsL1B5dG9yY2gtVU5ldA==">https://github.com/milesial/Pytorch-UNet<i class="fa fa-external-link-alt"></i></span><ul>
<li>Framework: PyTorch</li>
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<h2 id="Complete-Binary-Tree"><a href="#Complete-Binary-Tree" class="headerlink" title="Complete Binary Tree"></a>Complete Binary Tree</h2><p>Tree에 대한 기본적인 설명은 루비콘 팀 Martin Kim의 포스트를 참조한다. <span class="github-emoji" alias="bow" style fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f647.png?v8">🙇</span></p>
<p><strong>Ref: <span class="exturl" data-url="aHR0cHM6Ly9ibG9nLm1hcnRpbndvcmsuY28ua3IvdGhlb3J5LzIwMTgvMDkvMjIvd2hhdC1pcy10cmVlLmh0bWw=">https://blog.martinwork.co.kr/theory/2018/09/22/what-is-tree.html<i class="fa fa-external-link-alt"></i></span></strong></p>
<p>추가적으로 Tree에 대해 부가설명을 덧붙인다.<br>Tree의 정의는 다음 2가지를 따른다.</p>
<br>
<blockquote>
<p><strong>1. A root node</strong><br><strong>2. The remaining nodes are partitioned into $n(n \geq 0)$ disjoint sets $T_1, T_2, … , T_n$ ($T_i$ : subtrees of the root)</strong></p>
</blockquote>
<br>
<p>즉, 루트 노드가 존재하며, 0개 이상의 서브트리로 분리된 노드가 존재해야 한다.<br>이 중 Full Binary Tree는 Depth가 $k$일 때, 총 노드의 개수는 $2^k - 1$개인 트리이며,<br>Complete Binary Tree는 Depth가 $k$인 Full Binary Tree를 왼쪽에서부터 순차적으로 읽었을 때, $k-level$에서 Complete Binary Tree의 총 노드 수 $n$의 인덱스를 가진 노드가 존재하는 것을 말한다.</p>
<p>이 때, $k =\lceil\log_{2}(n + 1)\rceil$이 성립한다.<br>Complete Binary Tree는 마지막 레벨을 제외하고 모든 레벨이 채워져 있으며, 마지막 레벨의 모든 노드는 가능한 한 가장 왼쪽에 있기 때문에 공간의 낭비가 없어 배열로 구현하는 것이 효율적이다.</p>
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