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	<title>Python &#8211; BrilliantCode.net</title>
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		<title>使用 Ubuntu 作為 深度學習/機器學習/人工智慧之 平台</title>
		<link>https://www.brilliantcode.net/1012/setup-ubuntu-as-a-platform-of-deep-learning-using-cuda-toolkit-9-0-cudnn-anaconda-3-python-3-5-tensorflow/</link>
				<comments>https://www.brilliantcode.net/1012/setup-ubuntu-as-a-platform-of-deep-learning-using-cuda-toolkit-9-0-cudnn-anaconda-3-python-3-5-tensorflow/#comments</comments>
				<pubDate>Sat, 24 Mar 2018 19:07:15 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[Ubuntu]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Anaconda]]></category>
		<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Deep Learning]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Tensorflow]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=1012</guid>
				<description><![CDATA[<p>人工智慧相關的新興職業將會在未來五到十年內爆炸式增加，尤其一個更是未來不可或缺的職業——“AI訓練師”。(正面迎擊人機合作的時代：AI時代3大關鍵人才)<br />
小弟我是不太敢確定AI訓練師這個職業會不會在台灣火熱地成長，不過，很確定的是以全球來說，至少中國、歐美、新加坡絕對是如此！<br />
如此明顯的大趨勢之下，還不來學學這些新東西嗎？</p>
<p>本篇文章之目的為詳列系統環境建置的步驟、相關文章以及相關troubleshooting！<br />
【環境建置】：Ubuntu 17.10 + CUDA Toolkit 9.0 + cuDNN + Anaconda 3 + Python 3.5 + Tensorflow<br />
【相關文章】：Python基礎教學</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1012/setup-ubuntu-as-a-platform-of-deep-learning-using-cuda-toolkit-9-0-cudnn-anaconda-3-python-3-5-tensorflow/">使用 Ubuntu 作為 深度學習/機器學習/人工智慧之 平台</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>2</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">1012</post-id>	</item>
		<item>
		<title>Ubuntu 安裝 Python3.5, Tensorflow-gpu, jupyter</title>
		<link>https://www.brilliantcode.net/962/ubuntu-17-10-install-python3-5-tensorflow-gpu-jupyter/</link>
				<comments>https://www.brilliantcode.net/962/ubuntu-17-10-install-python3-5-tensorflow-gpu-jupyter/#respond</comments>
				<pubDate>Sat, 24 Mar 2018 16:41:17 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Ubuntu]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[Anaconda]]></category>
		<category><![CDATA[jupyter]]></category>
		<category><![CDATA[Python]]></category>
		<category><![CDATA[Tensorflow]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=962</guid>
				<description><![CDATA[<p>因為身邊剛好有個機會要建立一個環境，這個環境會是一台伺服器，上面運行Ubuntu，所有其他人可以用瀏覽器連上這台伺服器的jupyter並且使用Python和Tensorflow來完成顯卡運算需求。<br />
本文的內容是基於已完成下列條件的教學：<br />
(1).Nvidia 顯卡驅動<br />
(2).Nvidia CUDA Toolkit<br />
(3).Nvidia cuDNN</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/962/ubuntu-17-10-install-python3-5-tensorflow-gpu-jupyter/">Ubuntu 安裝 Python3.5, Tensorflow-gpu, jupyter</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">962</post-id>	</item>
		<item>
		<title>Google Colaboratory &#8211; Google提供的免費 Python + Tensorflow-GPU 開發環境</title>
		<link>https://www.brilliantcode.net/858/google-colaboratory-python-tensorflow-gpu-free/</link>
				<comments>https://www.brilliantcode.net/858/google-colaboratory-python-tensorflow-gpu-free/#respond</comments>
				<pubDate>Sat, 03 Feb 2018 18:09:13 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[Google Colaboratory]]></category>
		<category><![CDATA[NumPy]]></category>
		<category><![CDATA[Tensorflow]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=858</guid>
				<description><![CDATA[<p>哪裡有免費的Python + Tensorflow-GPU 開發環境？<br />
答案是Google Drive就有了！而且還免費！他叫做：Google Colaboratory<br />
這個功能實在是讓所有Python的教學者提供了一大福音！<br />
因為，不需要浪費時間在環境建置上，可以讓學生馬上進入狀況！<br />
至於環境建置的部分可以讓遇到問題的學生在課餘時找解答就好！如果想要建置私有的Tensorflow開發環境，可以參考這篇：使用 Ubuntu 作為 深度學習/機器學習/人工智慧之 平台</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/858/google-colaboratory-python-tensorflow-gpu-free/">Google Colaboratory &#8211; Google提供的免費 Python + Tensorflow-GPU 開發環境</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">858</post-id>	</item>
		<item>
		<title>Python3 &#038; Numpy &#8211; 教學、筆記</title>
		<link>https://www.brilliantcode.net/789/python3-6-turorial/</link>
				<comments>https://www.brilliantcode.net/789/python3-6-turorial/#comments</comments>
				<pubDate>Sun, 21 Jan 2018 16:46:49 +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=789</guid>
				<description><![CDATA[<p>從零開始學習Python的完整筆記都在這了！<br />
Python教學：<br />
變數使用、流程控制(for, while, if)、資料結構(List, Dictionary, Truple, Set)、模組(import)、檔案編輯(open, write, read, JSON)、錯誤處理(Try-catch)、顯示(print, format, repr, codec)、類別(建構子, 繼承, 覆載, 多型, 特殊用法)<br />
NumPy教學：<br />
建立陣列/矩陣、客製輸出(set_printoptions)、矩陣乘法(dot)、矩陣索引(indexing)、矩陣切片(slicing)、重新塑形(reshpe)、矩陣堆疊、矩陣分割(splitting)、檢視(view)、深度拷貝(deep copy)、使用陣列選取矩陣內容、使用布林矩陣選取矩陣內容、ix_函數、重製(repeat/tile)、堆疊(hstack/vstack)、線性代數、...</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/789/python3-6-turorial/">Python3 &#038; Numpy &#8211; 教學、筆記</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>1</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">789</post-id>	</item>
		<item>
		<title>Python3 教學 #05 (Ch9: Class: 繼承、建構子、多型、封裝、覆載)</title>
		<link>https://www.brilliantcode.net/761/python-3-6-class/</link>
				<comments>https://www.brilliantcode.net/761/python-3-6-class/#respond</comments>
				<pubDate>Sun, 21 Jan 2018 15:57:26 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=761</guid>
				<description><![CDATA[<p>本文會介紹Python的Class類別相關語法：<br />
Python Class繼承、多型、封裝、建構子、變數、父類別的屬性/方法如何使用、覆載的語法</p>
<p>Python也是物件導向程式語言，在學習這個部分時，可以帶入Java的觀念來記憶會更深刻喔！<br />
(以下語法皆以Python3.6撰寫。)</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/761/python-3-6-class/">Python3 教學 #05 (Ch9: Class: 繼承、建構子、多型、封裝、覆載)</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">761</post-id>	</item>
		<item>
		<title>Python3 教學 #04 (Ch6~Ch8: Try-catch 錯誤處理)</title>
		<link>https://www.brilliantcode.net/753/python3-6-try-catch/</link>
				<comments>https://www.brilliantcode.net/753/python3-6-try-catch/#respond</comments>
				<pubDate>Wed, 17 Jan 2018 16:33:03 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=753</guid>
				<description><![CDATA[<p>本章會介紹Python的Try-catch！<br />
Try-catch: try 執行, except 例外, else 出錯就執行這區塊, finally 任何狀況下都要執行的區塊, raise 拋出錯誤<br />
Python的錯誤處理架構與Java或是其他程式語言差異不大，透過其他程式語言個觀念來思考可以加速理解喔！<br />
藉由適當的錯誤處理，讓你的程式立於不敗之地～<br />
(以下語法皆以Python3.6撰寫。)</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/753/python3-6-try-catch/">Python3 教學 #04 (Ch6~Ch8: Try-catch 錯誤處理)</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">753</post-id>	</item>
		<item>
		<title>Python3 教學 #03 (Ch6~Ch8: Module、I/O)</title>
		<link>https://www.brilliantcode.net/726/python3-6-module-io/</link>
				<comments>https://www.brilliantcode.net/726/python3-6-module-io/#respond</comments>
				<pubDate>Wed, 17 Jan 2018 10:40:35 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=726</guid>
				<description><![CDATA[<p>本章會介紹Python的Module、I/O！<br />
Module(模組)： import 套件用法<br />
I/O(輸入/輸出)：包括open 開檔、read 讀檔、write 寫檔、印訊息 (print、repr、format)、codec 處理中文<br />
也會特別介紹如何針對.JSON檔做中文字的讀取、寫入！<br />
(以下語法皆以Python3.6撰寫。)</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/726/python3-6-module-io/">Python3 教學 #03 (Ch6~Ch8: Module、I/O)</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">726</post-id>	</item>
		<item>
		<title>Python3 教學 #02 (Ch5: List、Tuple、Set、Dictionary)</title>
		<link>https://www.brilliantcode.net/713/python3-6-list-tuple-set-dictionary/</link>
				<comments>https://www.brilliantcode.net/713/python3-6-list-tuple-set-dictionary/#comments</comments>
				<pubDate>Sun, 14 Jan 2018 17:27:31 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=713</guid>
				<description><![CDATA[<p>本章會介紹Python內建幾個重要的資料結構：List、Tuple、Set、Dictionary<br />
NumPy讓大家在使用Python時可以專注在資料處理邏輯上，不需要花時間在資料結構撰寫！<br />
在進行矩陣運算時也會用到許多這些資料結構的觀念，因此這部分的學習對於機器學習、深度學習、人工智慧都有深遠的影響！<br />
(以下語法教學都是以Python 3.6撰寫。)</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/713/python3-6-list-tuple-set-dictionary/">Python3 教學 #02 (Ch5: List、Tuple、Set、Dictionary)</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>2</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">713</post-id>	</item>
		<item>
		<title>Python3 教學 #01 (Ch1~Ch4: 環境說明、運算子、流程控制)</title>
		<link>https://www.brilliantcode.net/693/python-3-6-interpreter-calculator-control-flow/</link>
				<comments>https://www.brilliantcode.net/693/python-3-6-interpreter-calculator-control-flow/#comments</comments>
				<pubDate>Mon, 25 Dec 2017 22:10:48 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[教學]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=693</guid>
				<description><![CDATA[<p>本篇文章是基於這篇Python教學文件基礎之上，整理出Python較有特色或是易忘記的內容：<br />
IDLE使用、變數宣告、for迴圈、while迴圈、if判斷式、range()函數使用</p>
<p>希望對大家在學習Python的過程中有所幫助！<br />
若還未建置Python開發環境的人可以點這篇文章：OS X 安裝 Python 3 ; Windows直接安裝installer即可。<br />
(以下語法教學都是以Python 3.6撰寫)</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/693/python-3-6-interpreter-calculator-control-flow/">Python3 教學 #01 (Ch1~Ch4: 環境說明、運算子、流程控制)</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>2</slash:comments>
						<post-id xmlns="com-wordpress:feed-additions:1">693</post-id>	</item>
		<item>
		<title>OS X &#8211; Python Django (1)建置開發環境</title>
		<link>https://www.brilliantcode.net/651/mac-os-install-django-development-development-environment/</link>
				<comments>https://www.brilliantcode.net/651/mac-os-install-django-development-development-environment/#respond</comments>
				<pubDate>Sun, 15 Oct 2017 09:39:33 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Django]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[Mac]]></category>
		<category><![CDATA[Python]]></category>

		<guid isPermaLink="false">https://www.brilliantcode.net/?p=651</guid>
				<description><![CDATA[<p>先將Python安裝好，就可以開始建置Django的開發環境囉！<br />
不知道Python該怎麼安裝？點這裡：OS X 安裝 Python 3<br />
Django使用版本：1.11.6</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/651/mac-os-install-django-development-development-environment/">OS X &#8211; Python Django (1)建置開發環境</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
]]></description>
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