深度學習技術(shù)及其應(yīng)用課程教學大綱

1、深度學習技術(shù)及其應(yīng)用課程教學大綱Course Outline課程基本信息（Course Information）課程代碼（Course Code）MS318*學時（Credit Hours）32*學分（Credits）2*課程名稱（Course Title）（中文）深度學習技術(shù)及其應(yīng)用（英文）Deep Learning and Its Applications*課程性質(zhì)（Course Type）選修課授課對象（Target Audience）本科生*授課語言(Language of Instruction)英語*開課院系（School）致遠學院先修課程（Prerequisite）線性代數(shù)、概率

2、論、微積分、計算機程序設(shè)計授課教師（Instructor）俞凱課程網(wǎng)址(Course Webpage)*課程簡介（Description）本課程將全面的介紹近年發(fā)展起來的基于神經(jīng)網(wǎng)絡(luò)的深度學習技術(shù)的基本概念，主要結(jié)構(gòu)，核心方法和關(guān)鍵應(yīng)用。主要內(nèi)容包括：機器學習和神經(jīng)網(wǎng)絡(luò)的基本概念和算法，深度學習的主流結(jié)構(gòu)及多種不同的激活函數(shù)，深度學習的實用算法細節(jié)，深度學習的應(yīng)用例子。通過課程的學習，使同學們鞏固基礎(chǔ)數(shù)學及機器學習的基本概念和算法；掌握神經(jīng)網(wǎng)絡(luò)基本概念；掌握深度學習中的主要網(wǎng)絡(luò)結(jié)構(gòu)的基本概念和相關(guān)算法；了解具體應(yīng)用領(lǐng)域的背景知識、應(yīng)用相關(guān)的深度學習技術(shù)；掌握通用深度學習網(wǎng)絡(luò)的參數(shù)訓練、深度學習

3、的結(jié)構(gòu)變種、序列級深度學習的訓練和使用。*課程簡介（Description）This course will give a full picture of recently developed deep learning techniques. Basic concepts, main structures, core algorithms and key applications will be introduced in detail. Content includes: basic concepts and algorithms of machine learning and neura

4、l networks, popular network structures and activation functions of deep learning, algorithm details of deep learning and key application cases. The course will help students consolidate the knowledge of basic mathematics and fundamentals of machine learning; know the concepts of neural network; unde

5、rstand the main-stream techniques of deep learning including deep neural network (DNN), convolutional neural network (CNN) and recurrent neural network (RNN); learn the applications of deep learning including speech recognition, image recognition and natural language processing; learn the advances o

6、f deep learning including computational network, structured deep learning and sequence-to-sequence learning.課程教學大綱（course syllabus）*學習目標(Learning Outcomes)1鞏固基礎(chǔ)數(shù)學及機器學習的基本概念和算法。2掌握神經(jīng)網(wǎng)絡(luò)基本概念。3掌握深度學習中的主要網(wǎng)絡(luò)結(jié)構(gòu)的基本概念和相關(guān)算法。4了解具體應(yīng)用領(lǐng)域的背景知識、應(yīng)用相關(guān)的深度學習技術(shù)。5. 掌握通用深度學習網(wǎng)絡(luò)的參數(shù)訓練、深度學習的結(jié)構(gòu)變種、序列級深度學習的訓練和使用。*教學內(nèi)容、進度安排及要求(Cl

7、ass Schedule&Requirements)教學內(nèi)容學時教學方式作業(yè)及要求基本要求考查方式基本數(shù)學知識復習2課堂教學無掌握無機器學習基礎(chǔ)2課堂教學無掌握無神經(jīng)網(wǎng)絡(luò)概念2課堂教學無掌握無神經(jīng)網(wǎng)絡(luò)訓練及分析2課堂教學無掌握無深度神經(jīng)網(wǎng)絡(luò)初始化2課堂教學無掌握無深度神經(jīng)網(wǎng)絡(luò)訓練2課堂教學小作業(yè)掌握作業(yè)MXNet應(yīng)用講座2講座無掌握作業(yè)前沿論文閱讀與討論I2課堂討論演講掌握演講深度神經(jīng)網(wǎng)絡(luò)語音識別應(yīng)用2課堂教學無掌握無卷積神經(jīng)網(wǎng)絡(luò)2課堂教學小作業(yè)掌握作業(yè)卷積神經(jīng)網(wǎng)絡(luò)圖像識別應(yīng)用2課堂教學無掌握作業(yè)循環(huán)神經(jīng)網(wǎng)絡(luò)2課堂教學無掌握無長短時記憶網(wǎng)絡(luò)2課堂教學小作業(yè)掌握作業(yè)循環(huán)神經(jīng)網(wǎng)絡(luò)自然語言處

8、理應(yīng)用2課堂教學無掌握無結(jié)構(gòu)化深度學習和序列深度學習2課堂教學無掌握無前沿論文閱讀與討論II2課堂討論演講掌握演講*考核方式(Grading)100%為平時成績（小作業(yè)40%，大作業(yè)40%，演講20%，課堂表現(xiàn)5%額外分數(shù)）*教材或參考資料(Textbooks & Other Materials)1. Christopher Bishop. Pattern Recognition and Machine Learning. Springer Press.2. Dong Yu and Li Deng. Automatic Speech Recognition - A Deep Learning Approach. Springer Press.4. Yoshua