關於 unsupervised image classification ，我們在網路上蒐集到這些相關的討論、資訊與評價

感謝東吳巨量資料學院的胡學長，貢獻了大數據英文用語的part 2囉！
這次胡學長focus在「機器學習篇」，以及介紹三個因為機器學習出現而發展非常快速的領域，分別是「文字」、「影像」和「音訊」。
大家不用覺得機器學習離我們很遙遠，像是youtube的推薦系統、google翻譯以及siri的背後通通都是使用機器學習的演算法哦！而且其實概念並不難，有興趣的孩子可以多探究！
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🤖 胡哥+俐媽英文教室—機器學習篇 machine learning：
1️⃣ <機器學習四大類別>
* supervised learning 監督式學習
* semi-supervised learning 半監督式學習
* unsupervised learning 非監督式學習
* reinforcement learning 強化學習

2️⃣ <常見用語>
* cluster 分群
* classification 分類
* regression 迴歸
* model 模型
* parameter 參數
* predict 預測
* accuracy 準確率
* overfitting 過度擬合
* feature 特徵欄位
* label 目標欄位
* training data 訓練資料
* testing data 測試資料
* validation data 驗證資料
* standardization 資料標準化
* feature extraction 特徵提取
* dimension reduction 資料降維

3️⃣ <文字分析>
* text mining 文字探勘
* natural language process 自然語言處理
* text categorization 文本分類
* information retrieval 資訊檢索
* word segmentation 自動分詞
* machine translation 機器翻譯
* topic modeling 主題式分析
* sentiment analysis 文字情緒分析
* part of speech 文字詞性分析
* word embedding 詞向量轉換

4️⃣ <影像辨識>
* computer vision 電腦視覺
* image recognition 影像辨識
* image segmentation 影像切割
* image annotation 影像標注
* object detection 物件偵測
* face detection 人臉辨識

5️⃣ <音訊辨識>
* speech recognition 語音辨識
* signal extraction 訊號處理
* noise reduction 雜訊處理
* acoustic model 聲學模型
* time domain 時域
* frequency domain 頻域
* Fourier transform 傅立葉轉換
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真是隔行如隔山，有你們提供其他專業領域英文，大家都彼此受惠!
感謝胡哥🙏🏼～
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#俐媽英文教室 #俐媽英文教室徵稿中 #俐媽英文教室大數據篇 #謝謝胡哥 #東吳巨量資料學院 #大數據 #bigdata #AI #machinelearning

在大量資訊化的時代，大數據big data已然變得重要，感謝東吳巨量資料學院胡學長的提供，大家又有好料可以吸收了！
有興趣的孩子，不妨多深入研究哦！
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💽 胡哥+俐媽英文教室：
{大眾用語篇｝
💻 big data 大數據
💻 data analyst 資料分析師
💻 data scientist 資料科學家
💻 artificial intelligence (AI) 人工智慧
💻 machine learning 機器學習
💻 social network analysis (SNA) 社群網路分析
💻 cloud 雲端
💻 internet of things (IOT) 物聯網
💻 fintech (financial+technology) 金融科技
💻 block chain 區塊鏈
💻 information security 資訊安全
💻 statistics 統計學
💻 chatbot 聊天機器人

{進階用語篇}
🤖 programming language 程式語言
🤖 supervised learning 監督式學習
🤖 unsupervised learning 非監督式學習
🤖 reinforcement learning 強化學習
🤖 deep learning 深度學習
🤖 neural network 神經網路 (AlphaGo的重要原理)
🤖 image recognition 影像辨識
🤖 classification 分類
🤖 cluster 分群
🤖 regression 迴歸
🤖 algorithm 演算法
🤖 web crawler 網路爬蟲
🤖 database 資料庫
🤖 text mining 文字探勘
🤖 data mining 資料挖掘
🤖 data engineering 資料工程
🤖 data structure 資料結構
🤖 data storage 資料倉儲
🤖 data visualization 資料視覺化
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感謝胡哥🙏🏼
這麼棒的領域和分享，真心期待有part 2！
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#俐媽英文教室 #俐媽英文教室徵稿中 #謝謝胡哥 #東吳巨量資料學院 #大數據 #bigdata #bigdatatechnologies #AI #dataanalysis

【Talk】Prof. Zhengya Zhang (U Michigan): Neuromorphic Computing Using Sparse Codes: From Algorithm to Hardware (July 16, 2015(Thursday, 10:30am-12pm)

Invite you all to join it. 歡迎踴躍參加 !

Title: Neuromorphic Computing Using Sparse Codes: From Algorithm to Hardware

Date: July 16, 2015 ( Thursday, 10:30 am ~ 12:00 pm)

Place: ED528, 5F, Engineering Building 4, NCTU
交通大學(光復校區)工程四館5樓528室

Speaker: Prof. Zhengya Zhang (University of Michigan, Ann Arbor)

Abstract:
Some of the latest advances in computer vision have been built upon the understanding of the mammalian primary visual cortex (V1). The receptive fields of V1 neurons can be compared to the basis functions underlying natural images. Learning the receptive fields allows us to carry out complex vision processing, including efficient image encoding, feature detection, and classification. Sparse coding is one development in unsupervised machine learning for training a network of neurons using natural images to extract the receptive fields that resemble the V1 receptive fields. We explore the dynamics of the sparse coding algorithm for an efficient mapping onto practical hardware. Design considerations involving tuning network and neuron responses have a significant impact on the neuron spiking pattern that determines the fidelity of image processing and the efficiency of resource utilization. The spiking pattern can be further exploited to improve the performance and scalability of the hardware architecture. The soft neural computation is intrinsically error tolerant and many opportunities exist in approximating the neuron communication and computation in designing high-performance and energy-efficient image processing hardware.

Biography:
Zhengya Zhang received the B.Sc. degree from the University of Waterloo in Canada in 2003, and the M.S. and Ph.D. degrees from the University of California, Berkeley, in 2005 and 2009, respectively. Since 2009, he has been with the Department of Electrical Engineeringand Computer Science at the University of Michigan, Ann Arbor, where he is currently an Associate Professor. His research is in the area of low-power and high-performance VLSI circuits and systems for computing, communications and signal processing. Dr. Zhang received the Intel Early Career Faculty Award in 2013, the National Science Foundation CAREER Award in 2011, the David J. Sakrison Memorial Prize from UC Berkeley in 2009, and the Best Student Paper Award at the Symposium on VLSI Circuits in 2009. He is an Associate Editor of the IEEE Transactions on Circuits and Systems-I, II, and the IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

Host: 交大電子系楊家驤教授 Email: chy@nctu.edu.tw