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2012年博士毕业于清华大学计算机系。博士期间的研究方向包括计算机图形学、视觉、机器学习等，发表论文多篇。同年加入Hulu，先后从事自然语言处理、推荐算法等方面的研究工作。
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通过这本书的写作，我也提高了很多。原以为把自己会的东西写出来是一个很容易的事情，可是真正开始写作，才知道自己的学识是多么肤浅。通过一遍遍翻阅各种文献资料，一遍遍推敲每一个定义和公式，我也获得了之前从未有过的清晰理解。
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人工智能正改变着这个世界的方方面面，改变着人类历史的进程，也会成为每一个有为青年的必修课。很高兴能成为本书的作者，和大家一起去探索这个充满着朝气和活力的领域。
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百面机器学习：算法工程师带你去面试 参考文献
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[2] Friedman J H. Greedy function approximation: a gradient boosting machine[J]. Annals of Statistics, 2001, 29(5): 1189-1232.
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[3] Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. Computer Science, 2013.
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[6] Dhillon I S, Guan Y, Kulis B. Kernel k-means: spectral clustering and normalized cuts[C]//Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2004: 551-556.
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[7] Banerjee A, Dave R N. Validating clusters using the Hopkins statistic[C]// IEEE International Conference on Fuzzy Systems, 2004. Proceedings. IEEE, 2004: 149-153 vol.1.
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[9] Boyd S, Vandenberghe L. Convex optimization[M]. Cambridge University Press, 2004.
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[10] Nesterov Y. A method of solving a convex programming problem with convergence rate $O(1/k^2)$[C]// Soviet Mathematics Doklady. 1983: 372-376.
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[14] Shanno D F. Conditioning of quasi-Newton methods for function minimization[J]. Mathematics of Computation, 1970, 24(111): 647-656.
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[16] Abramson N, Braverman D, Sebestyen G. Pattern recognition and machine learning[M]. Academic Press, 1963.
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[17] He H, Garcia E A. Learning from imbalanced data[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263-1284.
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[18] Xu B, Wang N, Chen T, et al. Empirical evaluation of rectified activations in convolutional network[J]. Computer Science, 2015.
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[19] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
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