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	<title>NumPy &#8211; BrilliantCode.net</title>
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		<title>Numpy 筆記-#02 另存變數為 .npz</title>
		<link>https://www.brilliantcode.net/1832/numpy-note-02-save-variables-as-npz/</link>
				<comments>https://www.brilliantcode.net/1832/numpy-note-02-save-variables-as-npz/#respond</comments>
				<pubDate>Fri, 27 Sep 2019 07:38:28 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[NumPy]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=1832</guid>
				<description><![CDATA[<p>本文將介紹在進行一些運算最常使用到的功能之一：如何儲存變數、該怎麼讀取.npz檔。其實，使用Numpy就能簡單地達成這個任務(numpy.savez)，而且還附有壓縮功能的方法（numpy.savez_compressed)。</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1832/numpy-note-02-save-variables-as-npz/">Numpy 筆記-#02 另存變數為 .npz</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
]]></description>
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						<post-id xmlns="com-wordpress:feed-additions:1">1832</post-id>	</item>
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		<title>Numpy 筆記-#01 卷積神經網路的Padding</title>
		<link>https://www.brilliantcode.net/1821/numpy-notes-01-padding-in-convolutional-neural-network/</link>
				<comments>https://www.brilliantcode.net/1821/numpy-notes-01-padding-in-convolutional-neural-network/#respond</comments>
				<pubDate>Sun, 22 Sep 2019 15:56:18 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[CNN]]></category>
		<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[NumPy]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=1821</guid>
				<description><![CDATA[<p>如果你有恆心、毅力僅使用Numpy打造自己的卷積神經網路(CNN)就一定會碰到一個問題，CNN裡面會用到的Padding該怎麼實作？其實Numpy已經幫你準備好這項功能(當然這個功能應該不僅止於此)。總之，本文就是來教該如何使用numpy.pad( )。</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1821/numpy-notes-01-padding-in-convolutional-neural-network/">Numpy 筆記-#01 卷積神經網路的Padding</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
]]></description>
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		<slash:comments>0</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">1821</post-id>	</item>
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		<title>NumPy 1.14 教學 – #09 ix_( )函數、線性代數(Linear Algebra)、重複(Repeat)、堆疊(Stack)</title>
		<link>https://www.brilliantcode.net/1263/numpy-tutorial-ix-linear-algebra-repeat-stack/</link>
				<comments>https://www.brilliantcode.net/1263/numpy-tutorial-ix-linear-algebra-repeat-stack/#respond</comments>
				<pubDate>Fri, 30 Nov 2018 09:50:12 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[NumPy]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=1263</guid>
				<description><![CDATA[<p>NumPy提供了一個很有趣的函數 ix_ ，一剛開始還有點看不太懂官方文件到底是想表示什麼？<br />
仔細閱讀其他的範例之後發現原來 ix_ 函數的用途應該算是很多樣的。<br />
此外還會介紹np.repeat、np.tile、np.hstack、np.vstack等用法。<br />
本文#09應該算是 NumPy教學系列文的最後一篇。<br />
不過，未來若有碰到什麼奇妙的用法也還是會一併補充上來。 :D</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1263/numpy-tutorial-ix-linear-algebra-repeat-stack/">NumPy 1.14 教學 – #09 ix_( )函數、線性代數(Linear Algebra)、重複(Repeat)、堆疊(Stack)</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
]]></description>
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		<slash:comments>0</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">1263</post-id>	</item>
		<item>
		<title>NumPy 1.14 教學 – #08 用布林陣列當索引取值(Indexing with boolean array)</title>
		<link>https://www.brilliantcode.net/1206/numpy-tutorial-indexing-with-boolean-array/</link>
				<comments>https://www.brilliantcode.net/1206/numpy-tutorial-indexing-with-boolean-array/#respond</comments>
				<pubDate>Wed, 05 Sep 2018 04:43:48 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[NumPy]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=1206</guid>
				<description><![CDATA[<p>本文將會介紹如何以判斷式對篩選整個矩陣符合條件的元素，進而產生布林矩陣，以便我們用在整個矩陣的數值修改或判斷！這個用法在進行矩陣運算時很常用到。</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1206/numpy-tutorial-indexing-with-boolean-array/">NumPy 1.14 教學 – #08 用布林陣列當索引取值(Indexing with boolean array)</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
]]></description>
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		<slash:comments>0</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">1206</post-id>	</item>
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		<title>NumPy 1.14 教學 – #07 用陣列當索引取值(Indexing with array of indices)</title>
		<link>https://www.brilliantcode.net/1183/numpy-tutorial-indexing-with-array-of-indices/</link>
				<comments>https://www.brilliantcode.net/1183/numpy-tutorial-indexing-with-array-of-indices/#respond</comments>
				<pubDate>Sat, 14 Jul 2018 11:09:12 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[NumPy]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=1183</guid>
				<description><![CDATA[<p>本文將介紹NumPy的矩陣/陣列的進階索引(argmax)方法，算是蠻特別的，目前我也還不清楚這個功能要用在哪裡？因為平常我們也會經常閱讀別人的程式，所以瞭解這些技巧也是必備技能之一！就當自我訓練吧～</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1183/numpy-tutorial-indexing-with-array-of-indices/">NumPy 1.14 教學 – #07 用陣列當索引取值(Indexing with array of indices)</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
]]></description>
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		<slash:comments>0</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">1183</post-id>	</item>
		<item>
		<title>NumPy 1.14 教學 – #06 簡易指定(Simple Assignments), 檢視(Views), 深度拷貝(Deep Copy)</title>
		<link>https://www.brilliantcode.net/1130/numpy-tutorial-simple-assignments-views-deep-copy/</link>
				<comments>https://www.brilliantcode.net/1130/numpy-tutorial-simple-assignments-views-deep-copy/#respond</comments>
				<pubDate>Fri, 08 Jun 2018 12:05:00 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[NumPy]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=1130</guid>
				<description><![CDATA[<p>NumPy提供了簡易指定Simple Assignments、檢視View、深度拷貝Deep Copy等方法，本文就會針對這幾種方法的差異做介紹！<br />
這是因為陣列這類包含大量指標的變數，對於程式語言來說，通常會兩種複製的方法，一種是類似於捷徑的做法，另一種則是以建立完整的內容來達成相同的效果。但兩者在使用上會有不同的效果！在撰寫時也必須要視情況而定！</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1130/numpy-tutorial-simple-assignments-views-deep-copy/">NumPy 1.14 教學 – #06 簡易指定(Simple Assignments), 檢視(Views), 深度拷貝(Deep Copy)</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
]]></description>
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		<slash:comments>0</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">1130</post-id>	</item>
		<item>
		<title>NumPy 1.14 教學 &#8211; #05 形狀操作、矩陣互相堆疊(Stacking)、矩陣切割(Splitting)</title>
		<link>https://www.brilliantcode.net/1111/numpy-tutorial-shape-manipulation-stack-split-matrices/</link>
				<comments>https://www.brilliantcode.net/1111/numpy-tutorial-shape-manipulation-stack-split-matrices/#respond</comments>
				<pubDate>Tue, 24 Apr 2018 14:52:42 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[NumPy]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=1111</guid>
				<description><![CDATA[<p>NumPy也提供了許多改變矩陣形狀、堆疊(Stacking)和切割(Splitting)的方法，這些功能也頗為實用！<br />
本文將介紹以下方法：reshape, ravel, vstack, hstack, vsplit, hsplit</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1111/numpy-tutorial-shape-manipulation-stack-split-matrices/">NumPy 1.14 教學 &#8211; #05 形狀操作、矩陣互相堆疊(Stacking)、矩陣切割(Splitting)</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
]]></description>
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		<slash:comments>0</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">1111</post-id>	</item>
		<item>
		<title>NumPy 1.14 教學 &#8211; #04 索引(Indexing)、切片(Slicing)、迭代(Iterating)、From Function</title>
		<link>https://www.brilliantcode.net/1093/numpy-1-14-tutorial-04-indexing-slicing-iterating-from-function/</link>
				<comments>https://www.brilliantcode.net/1093/numpy-1-14-tutorial-04-indexing-slicing-iterating-from-function/#respond</comments>
				<pubDate>Mon, 23 Apr 2018 13:54:37 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[NumPy]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=1093</guid>
				<description><![CDATA[<p>本篇將介紹如何使用NumPy對於陣列依據索引值(Indexing)存取資料、切片(Slicing)以及使用迴圈對1維矩陣、多維矩陣迭代取值等方法。最特別的是，本篇會介紹From Function的使用方法以及其用途！</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1093/numpy-1-14-tutorial-04-indexing-slicing-iterating-from-function/">NumPy 1.14 教學 &#8211; #04 索引(Indexing)、切片(Slicing)、迭代(Iterating)、From Function</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
]]></description>
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		<slash:comments>0</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">1093</post-id>	</item>
		<item>
		<title>NumPy 1.14 教學 &#8211; #03 基本操作(加減乘除、矩陣乘法、取代)</title>
		<link>https://www.brilliantcode.net/1062/numpy-tutorial-basic-operations/</link>
				<comments>https://www.brilliantcode.net/1062/numpy-tutorial-basic-operations/#comments</comments>
				<pubDate>Tue, 03 Apr 2018 12:20:35 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[NumPy]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=1062</guid>
				<description><![CDATA[<p>本文將會介紹NumPy矩陣之間的加減乘除(包含矩陣乘法)、取代、屬性以及常用方法(dot, sum, min, max, mean, cumsum, sqrt, add, exp,..)！</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1062/numpy-tutorial-basic-operations/">NumPy 1.14 教學 &#8211; #03 基本操作(加減乘除、矩陣乘法、取代)</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
]]></description>
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		<slash:comments>6</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">1062</post-id>	</item>
		<item>
		<title>NumPy 1.14 教學 &#8211; #02 如何印出陣列以及格式設定(np.set_printoptions)</title>
		<link>https://www.brilliantcode.net/1045/numpy-tutorial-how-to-print-array/</link>
				<comments>https://www.brilliantcode.net/1045/numpy-tutorial-how-to-print-array/#respond</comments>
				<pubDate>Thu, 29 Mar 2018 05:34:10 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[NumPy]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=1045</guid>
				<description><![CDATA[<p>本篇算是比較短篇幅的單元，在這將會說明該如何印出NumPy的陣列以及如何使用np.set_printoptions方法來設定列印格式！同時也會利用這個機會稍微使用一下reshape函數！</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1045/numpy-tutorial-how-to-print-array/">NumPy 1.14 教學 &#8211; #02 如何印出陣列以及格式設定(np.set_printoptions)</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
]]></description>
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