“大数据智能-参考文献”版本间的差异
来自iCenter Wiki
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* Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman. Mining of massive datasets. Cambridge University Press, 2014. [http://www.mmds.org/ MMDS_book] | * Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman. Mining of massive datasets. Cambridge University Press, 2014. [http://www.mmds.org/ MMDS_book] | ||
# Vijay Agneeswaran, Real-Time Applications with Storm, Spark, and More Hadoop Alternatives, 2014. | # Vijay Agneeswaran, Real-Time Applications with Storm, Spark, and More Hadoop Alternatives, 2014. | ||
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= 深度学习 = | = 深度学习 = | ||
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# Google brain team, TensorFlow: Large-scale machine learning on heterogeneous systems, whitepaper, 2015. | # Google brain team, TensorFlow: Large-scale machine learning on heterogeneous systems, whitepaper, 2015. | ||
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| + | # 刘知远，崔安欣等，大数据智能：互联网时代的机器学习和自然语言处理技术. 电子工业出版社，2016年。 | ||
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| + | == 计算机围棋 == | ||
# Mastering the game of Go with deep neural networks and tree search, nature 2015. | # Mastering the game of Go with deep neural networks and tree search, nature 2015. | ||
2017年6月10日 (六) 12:24的版本
数据挖掘
- Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman. Mining of massive datasets. Cambridge University Press, 2014. MMDS_book
- Vijay Agneeswaran, Real-Time Applications with Storm, Spark, and More Hadoop Alternatives, 2014.
深度学习
- Yoshua Bengio, Ian Goodfellow, Aaron Courville, Deep Learning, MIT Press, 2016. DeepLearningBook
- Google brain team, TensorFlow: Large-scale machine learning on heterogeneous systems, whitepaper, 2015.
大数据智能
- 刘知远，崔安欣等，大数据智能：互联网时代的机器学习和自然语言处理技术. 电子工业出版社，2016年。
计算机围棋
- Mastering the game of Go with deep neural networks and tree search, nature 2015.
- Better Computer Go Player with Neural Network and Long-term Prediction, ICLR 2016.
- Pachi: State of the art open source Go program, Advances in computer games, Springer Berlin Heidelberg, 2011.
- Training Deep Convolutional Neural Networks to Play Go, JMLR 2015.
