10702 工程學群 資訊工程學系

深度學習

吳尚鴻 教授

資訊工程學系
國立清華大學資訊工程學系    教授 
國立台灣大學資訊工程學系       博士

【教學】 機器學習理論、雲端資料庫、APP創業與實作
【研究】 機器學習、巨量資料處理、App 智能
  http://www.cs.nthu.edu.tw/~shwu/
【榮譽】 New Faculty Research Award, NTHU, 2015 
  Outstanding Research Award, EECS, NTHU, 2014
Outstanding Teaching Award, EECS, NTHU, 2013
IBM Ph.D. Fellowship Award, 2008 (70/575 worldwide)


News

最新公告

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Syllabus

課程大綱

This class introduces the concepts and practices of deep learning. The course consists of three parts. In the first part, we give a quick introduction of classical machine learning and review some key concepts required to understand deep learning.In the second part......
 
  

Description
This class introduces the concepts and practices of deep learning. The course consists of three parts. In the first part, we give a quick introduction of classical machine learning and review some key concepts required to understand deep learning.In the second part, we discuss how deep learning differs from classical machine learning and explain why it is effective in dealing with complex problems such as the image and natural language processing. Various CNN and RNN models will becovered. In the third part, we introduce the deep reinforcement learning and its applications.This course also gives coding labs. We will use Python 3 as the main programming language throughout the course. Some popular machine learning libraries such as Scikit-learn and Tensorflow will be used and explained in detials.

 
 
 

Syllabus

 Lecture 01   Introduction/Scientific Python 101
 Lecture 02    Linear Algebra/Data Exploration & PCA
 Lecture 03 Probability & Information Theory/Decision Trees & Random Forest
 Lecture 04 Numerical Optimization/Perceptron & Adaline/Regression
 Lecture 05 Learning Theory & Regularization /Regularization
 Lecture 06 Probabilistic Models/Logistic Regression & Metrics
 Lecture 07 Non-Parametric Methods & SVMs/SVMs & Scikit-Learn Pipelines
 Lecture 08 Cross Validation & Ensembling/CV & Ensembling
 Competition01   Predicting Appropriate Response
 Lecture 09 Large-Scale Machine Learning
 Lecture 10 Neural Networks: Design/TensorFlow101 & Word2Vec
 Lecture 11 Neural Networks: Optimization & Regularization
 Lecture 12 Convolutional Neural Networks/
  Nuts and Bolts of Convolutional Neural Networks/Visualization and Style Transfer
 Competition 02 Image Object Detection & Localization
 Lecture 13 Recurrent Neural Networks/Seq2Seq Learning for Machine Translation
 Competition 03 Image Caption
 Lecture 14 Unsupervised Learning/Autoencoders/GANs
 Competition 04 Reverse Image Caption 
 Lecture 15 Semisupervised/Transfer Learning and the Future
 Lecture 16 Reinforcement Learning/Q-learning
 Lecture 17 Deep Reinforcement Learning/ DQN & Policy Network
 Competition 05 You Draw I Draw
  
  

 

 
Online Courses 
CS231n: Convolutional Neural Networks for Visual Recognition, Stanford
CS224d: Deep Learning for Natural Language Processing, Stanford
CS 294: Deep Reinforcement Learning, Berkeley
MIT 6.S094: Deep Learning for Self-Driving Cars, MIT

Keyword

關鍵字

  • 深度學習
  • Deep Learning
  • Scientific Python
  • Neural Networks
  • Numerical Optimization

Teachers

吳尚鴻 教授

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