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<title>新手村逃脫！初心者的 Python 機器學習攻略 — 新手村逃脫！初心者的 Python 機器學習攻略 1.0.0 documentation</title>
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<h1>新手村逃脫！初心者的 Python 機器學習攻略<a class="headerlink" href="#python" title="Permalink to this headline">¶</a></h1>
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<p>本書處於 Early Release、瘋狂校稿階段。</p>
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<p>「新手村逃脫！初心者的 Python 機器學習攻略」是一本寫給對於機器學習有興趣、並想使用 Python 程式語言實作的入門書。內容是改寫我在 2017 年 iT 邦幫忙鐵人賽：「R 語言使用者的 Python 學習筆記」的系列文章而成，主要針對能撰寫 Python 程式、對於機器學習理論沒有基礎卻有興趣的讀者，透過本書能夠按圖索驥，走出新手村，成功一轉！</p>
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<li class="toctree-l2"><a class="reference internal" href="06-classification.html#自訂羅吉斯迴歸類別-LogitReg">自訂羅吉斯迴歸類別 LogitReg</a></li>
<li class="toctree-l2"><a class="reference internal" href="06-classification.html#二元分類延伸至多元分類：One-versus-rest">二元分類延伸至多元分類：One versus rest</a></li>
<li class="toctree-l2"><a class="reference internal" href="06-classification.html#二元分類延伸至多元分類：Softmax-函式">二元分類延伸至多元分類：Softmax 函式</a></li>
<li class="toctree-l2"><a class="reference internal" href="06-classification.html#兩種表示類別向量的形式">兩種表示類別向量的形式</a></li>
<li class="toctree-l2"><a class="reference internal" href="06-classification.html#延伸閱讀">延伸閱讀</a></li>
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<li class="toctree-l1"><a class="reference internal" href="07-performance.html">表現的評估</a><ul>
<li class="toctree-l2"><a class="reference internal" href="07-performance.html#如何評估機器學習演算方法">如何評估機器學習演算方法</a></li>
<li class="toctree-l2"><a class="reference internal" href="07-performance.html#評估數值預測任務的表現">評估數值預測任務的表現</a></li>
<li class="toctree-l2"><a class="reference internal" href="07-performance.html#評估類別預測任務的表現">評估類別預測任務的表現</a></li>
<li class="toctree-l2"><a class="reference internal" href="07-performance.html#自訂計算評估指標的類別-ClfMetrics">自訂計算評估指標的類別 <code class="docutils literal notranslate"><span class="pre">ClfMetrics</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="07-performance.html#誤差的來源">誤差的來源</a></li>
<li class="toctree-l2"><a class="reference internal" href="07-performance.html#減少訓練誤差">減少訓練誤差</a></li>
<li class="toctree-l2"><a class="reference internal" href="07-performance.html#減少訓練誤差與測試誤差的間距">減少訓練誤差與測試誤差的間距</a></li>
<li class="toctree-l2"><a class="reference internal" href="07-performance.html#延伸閱讀">延伸閱讀</a></li>
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<li class="toctree-l1"><a class="reference internal" href="08-deep-learning.html">深度學習入門</a><ul>
<li class="toctree-l2"><a class="reference internal" href="08-deep-learning.html#什麼是深度學習">什麼是深度學習</a></li>
<li class="toctree-l2"><a class="reference internal" href="08-deep-learning.html#為何深度學習">為何深度學習</a></li>
<li class="toctree-l2"><a class="reference internal" href="08-deep-learning.html#什麼是-Keras">什麼是 Keras</a></li>
<li class="toctree-l2"><a class="reference internal" href="08-deep-learning.html#為何-Keras">為何 Keras</a></li>
<li class="toctree-l2"><a class="reference internal" href="08-deep-learning.html#撰寫-Keras-的步驟">撰寫 Keras 的步驟</a></li>
<li class="toctree-l2"><a class="reference internal" href="08-deep-learning.html#前向傳播">前向傳播</a></li>
<li class="toctree-l2"><a class="reference internal" href="08-deep-learning.html#反向傳播">反向傳播</a></li>
<li class="toctree-l2"><a class="reference internal" href="08-deep-learning.html#自訂深度學習類別-DeepLearning">自訂深度學習類別 DeepLearning</a></li>
<li class="toctree-l2"><a class="reference internal" href="08-deep-learning.html#MNIST-資料與時裝-MNIST-資料">MNIST 資料與時裝 MNIST 資料</a></li>
<li class="toctree-l2"><a class="reference internal" href="08-deep-learning.html#延伸閱讀">延伸閱讀</a></li>
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<li class="toctree-l1"><a class="reference internal" href="09-appendix-a.html">附錄 A</a></li>
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