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Social Computing and Big Data Analytics 社群運算與大數據分析

Tamkang University. Tamkang University. Deep Learning with Theano and Keras in Python (Python Theano 和 Keras 深度學習 ). Social Computing and Big Data Analytics 社群運算與大數據分析. 1042SCBDA09 MIS MBA (M2226) (8628) Wed, 8,9, (15:10-17:00) (B309). Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授

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Social Computing and Big Data Analytics 社群運算與大數據分析

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  1. Tamkang University Tamkang University Deep Learning with Theano and Keras in Python (Python Theano 和 Keras 深度學習) Social Computing and Big Data Analytics社群運算與大數據分析 1042SCBDA09 MIS MBA (M2226) (8628) Wed, 8,9, (15:10-17:00) (B309) Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授 Dept. of Information Management, Tamkang University 淡江大學資訊管理學系 http://mail. tku.edu.tw/myday/ 2016-04-27

  2. 課程大綱 (Syllabus) 週次 (Week) 日期 (Date) 內容 (Subject/Topics) 1 2016/02/17 Course Orientation for Social Computing and Big Data Analytics (社群運算與大數據分析課程介紹) 2 2016/02/24 Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data (資料科學與大數據分析：探索、分析、視覺化與呈現資料) 3 2016/03/02 Fundamental Big Data: MapReduce Paradigm, Hadoop and Spark Ecosystem (大數據基礎：MapReduce典範、 Hadoop與Spark生態系統)

  3. 課程大綱 (Syllabus) 週次 (Week) 日期 (Date) 內容 (Subject/Topics) 4 2016/03/09 Big Data Processing Platforms with SMACK: Spark, Mesos, Akka, Cassandra and Kafka (大數據處理平台SMACK： Spark, Mesos, Akka, Cassandra, Kafka) 5 2016/03/16 Big Data Analytics with Numpy in Python (Python Numpy 大數據分析) 6 2016/03/23 Finance Big Data Analytics with Pandas in Python (Python Pandas 財務大數據分析) 7 2016/03/30 Text Mining Techniques and Natural Language Processing (文字探勘分析技術與自然語言處理) 8 2016/04/06 Off-campus study (教學行政觀摩日)

  4. 課程大綱 (Syllabus) 週次 (Week) 日期 (Date) 內容 (Subject/Topics) 9 2016/04/13 Social Media Marketing Analytics (社群媒體行銷分析) 10 2016/04/20 期中報告 (Midterm Project Report) 11 2016/04/27 Deep Learning with Theano and Keras in Python (Python Theano 和 Keras 深度學習) 12 2016/05/04 Deep Learning with Google TensorFlow (Google TensorFlow 深度學習) 13 2016/05/11 Sentiment Analysis on Social Media with Deep Learning (深度學習社群媒體情感分析)

  5. 課程大綱 (Syllabus) 週次 (Week) 日期 (Date) 內容 (Subject/Topics) 14 2016/05/18 Social Network Analysis (社會網絡分析) 15 2016/05/25 Measurements of Social Network (社會網絡量測) 16 2016/06/01 Tools of Social Network Analysis (社會網絡分析工具) 17 2016/06/08 Final Project Presentation I (期末報告 I) 18 2016/06/15 Final Project Presentation II (期末報告 II)

  6. Deep Learning with Theano and Keras in Python

  7. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.

  8. Source: LeCun, Yann, YoshuaBengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.

  9. Sebastian Raschka (2015), Python Machine Learning, Packt Publishing Source: http://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130

  10. Sunila Gollapudi (2016), Practical Machine Learning, Packt Publishing Source: http://www.amazon.com/Practical-Machine-Learning-Sunila-Gollapudi/dp/178439968X

  11. Machine Learning Models Deep Learning Kernel Association rules Ensemble Decision tree Dimensionality reduction Clustering Regression Analysis Bayesian Instance based Source: SunilaGollapudi (2016), Practical Machine Learning, Packt Publishing

  12. Neural networks (NN)1960 Source: SunilaGollapudi (2016), Practical Machine Learning, Packt Publishing

  13. Multilayer Perceptrons (MLP)1985 Source: SunilaGollapudi (2016), Practical Machine Learning, Packt Publishing

  14. Restricted Boltzmann Machine (RBM)1986 Source: SunilaGollapudi (2016), Practical Machine Learning, Packt Publishing

  15. Support Vector Machine(SVM) 1995 Source: SunilaGollapudi (2016), Practical Machine Learning, Packt Publishing

  16. Hinton presents the Deep Belief Network (DBN)New interests in deep learning and RBMState of the art MNIST2005 Source: SunilaGollapudi (2016), Practical Machine Learning, Packt Publishing

  17. Deep Recurrent Neural Network (RNN)2009 Source: SunilaGollapudi (2016), Practical Machine Learning, Packt Publishing

  18. Convolutional DBN 2010 Source: SunilaGollapudi (2016), Practical Machine Learning, Packt Publishing

  19. Max-Pooling CDBN 2011 Source: SunilaGollapudi (2016), Practical Machine Learning, Packt Publishing

  20. Neural Networks Input Layer (X) Hidden Layer (H) Output Layer (Y) X1 Y X2 Source: https://www.youtube.com/watch?v=bxe2T-V8XRs&index=1&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU

  21. Deep LearningGeoffrey Hinton Yann LeCunYoshua BengioAndrew Y. Ng

  22. Geoffrey HintonGoogle University of Toronto Source: https://en.wikipedia.org/wiki/Geoffrey_Hinton

  23. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.

  24. Deep Learning Source: LeCun, Yann, YoshuaBengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.

  25. Deep Learning Source: LeCun, Yann, YoshuaBengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.

  26. Deep Learning Source: LeCun, Yann, YoshuaBengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.

  27. Deep Learning Source: LeCun, Yann, YoshuaBengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.

  28. Recurrent Neural Network (RNN) Source: LeCun, Yann, YoshuaBengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.

  29. From image to text Source: LeCun, Yann, YoshuaBengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.

  30. From image to text Image: deep convolution neural network (CNN)Text: recurrent neural network (RNN) Source: LeCun, Yann, YoshuaBengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.

  31. CS224d: Deep Learning for Natural Language Processing http://cs224d.stanford.edu/

  32. Recurrent Neural Networks (RNNs) Source: http://cs224d.stanford.edu/lectures/CS224d-Lecture8.pdf

  33. X Y Hours Sleep Hours Study Score 3 5 75 5 1 82 10 2 93 8 3 ? Source: https://www.youtube.com/watch?v=bxe2T-V8XRs&index=1&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU

  34. X Y Hours Sleep Hours Study Score 3 5 75 Training 5 1 82 10 2 93 8 3 ? Testing Source: https://www.youtube.com/watch?v=bxe2T-V8XRs&index=1&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU

  35. Training a Network=Minimize the Cost Function Source: https://www.youtube.com/watch?v=bxe2T-V8XRs&index=1&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU

  36. Neural Networks Source: https://www.youtube.com/watch?v=bxe2T-V8XRs&index=1&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU

  37. Neural Networks Input Layer (X) Hidden Layer (H) Output Layer (Y) X1 Y X2 Source: https://www.youtube.com/watch?v=bxe2T-V8XRs&index=1&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU

  38. Neural Networks Input Layer (X) Hidden Layers (H) Output Layer (Y) Deep Neural Networks Deep Learning Source: https://www.youtube.com/watch?v=bxe2T-V8XRs&index=1&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU

  39. Neural Networks Input Layer (X) Hidden Layer (H) Output Layer (Y) Neuron Synapse Synapse Neuron X1 Y X2 Source: https://www.youtube.com/watch?v=bxe2T-V8XRs&index=1&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU

  40. Neuron and Synapse Source: https://en.wikipedia.org/wiki/Neuron

  41. Neurons 2 Bipolar neuron 1 Unipolar neuron 3 Multipolar neuron 4 Pseudounipolar neuron Source: https://en.wikipedia.org/wiki/Neuron

  42. Neural Networks Input Layer (X) Hidden Layer (H) Output Layer (Y) HoursSleep Score HoursStudy Source: https://www.youtube.com/watch?v=bxe2T-V8XRs&index=1&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU

  43. Neural Networks Input Layer (X) Hidden Layer (H) Output Layer (Y) Source: https://www.youtube.com/watch?v=P2HPcj8lRJE&list=PLjJh1vlSEYgvGod9wWiydumYl8hOXixNu&index=2

  44. Convolutional Neural Networks (CNNs / ConvNets) http://cs231n.github.io/convolutional-networks/

  45. A regular 3-layer Neural Network http://cs231n.github.io/convolutional-networks/

  46. A ConvNet arranges its neurons in three dimensions (width, height, depth) http://cs231n.github.io/convolutional-networks/

  47. The activations of an example ConvNet architecture. http://cs231n.github.io/convolutional-networks/

  48. ConvNets http://cs231n.github.io/convolutional-networks/

  49. ConvNets http://cs231n.github.io/convolutional-networks/

  50. ConvNets http://cs231n.github.io/convolutional-networks/

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