• 2주에 한번 월요일, 저녁 7시 30분 ~ 10시 30분. 역삼

교재

PART I

• SML 교재 : Mastering Machine Learning with scikit-learn - http://www.amazon.com/Mastering-Machine-Learning-scikit-learn-Hackeling/dp/1783988363
• DML 교재 : DEEP LEARNING - http://www.iro.umontreal.ca/~bengioy/dlbook/
• PGM 부교재 : Building Probabilistic Graphical Models with Python - https://www.packtpub.com/big-data-and-business-intelligence/building-probabilistic-graphical-models-python
• theano 실습 1 - http://deeplearning.net/software/theano/tutorial/index.html
• caffe 실습 1 - http://caffe.berkeleyvision.org/tutorial/

PART II

• SML 교재 : Mastering Machine Learning with scikit-learn - http://www.amazon.com/Mastering-Machine-Learning-scikit-learn-Hackeling/dp/1783988363
• DML 교재 : DEEP LEARNING - http://www.iro.umontreal.ca/~bengioy/dlbook/
• theano 실습 1 - http://deeplearning.net/software/theano/tutorial/index.html
• caffe 실습 1 - http://caffe.berkeleyvision.org/tutorial/

PART III

• SML 교재 : Mastering Machine Learning with scikit-learn - http://www.amazon.com/Mastering-Machine-Learning-scikit-learn-Hackeling/dp/1783988363
• SML-스파크 교재 : Advanced Analytics with Spark - http://www.amazon.com/Advanced-Analytics-Spark-Patterns-Learning/dp/1491912766
• DML 교재 : DEEP LEARNING - http://www.iro.umontreal.ca/~bengioy/dlbook/
• theano 실습 2 - Deep Learning Tutorial - http://deeplearning.net/tutorial/deeplearning.pdf
• tensorflow 실습 1 - https://www.tensorflow.org/