@@ -202,4 +202,4 @@ This chapter introduces *Linear Regression* and how to train and test this model
4. Bishop C M. Pattern recognition[J]. Machine Learning, 2006, 128.
<br/>
<arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><imgalt="Common Creative License"style="border-width:0"src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png"/></a> This tutorial was created and published with [Creative Common License 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/).
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
@@ -244,7 +244,7 @@ This chapter introduces *Linear Regression* and how to train and test this model
4. Bishop C M. Pattern recognition[J]. Machine Learning, 2006, 128.
<br/>
<arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><imgalt="Common Creative License"style="border-width:0"src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png"/></a> This tutorial was created and published with [Creative Common License 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/).
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
@@ -470,4 +470,4 @@ Semantic Role Labeling is an important intermediate step in a wide range of natu
10. Zhou J, Xu W. [End-to-end learning of semantic role labeling using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
@@ -512,7 +512,7 @@ Semantic Role Labeling is an important intermediate step in a wide range of natu
10. Zhou J, Xu W. [End-to-end learning of semantic role labeling using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
@@ -728,4 +728,4 @@ End-to-end neural machine translation is a recently developed way to perform mac
5. Papineni K, Roukos S, Ward T, et al. [BLEU: a method for automatic evaluation of machine translation](http://dl.acm.org/citation.cfm?id=1073135)[C]//Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 2002: 311-318.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
@@ -770,7 +770,7 @@ End-to-end neural machine translation is a recently developed way to perform mac
5. Papineni K, Roukos S, Ward T, et al. [BLEU: a method for automatic evaluation of machine translation](http://dl.acm.org/citation.cfm?id=1073135)[C]//Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 2002: 311-318.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
@@ -293,7 +293,7 @@ This tutorial describes a few basic Deep Learning models viz. Softmax regression
7. Deng, Li, Michael L. Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, and Geoffrey E. Hinton. ["Binary coding of speech spectrograms using a deep auto-encoder."](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.1908&rep=rep1&type=pdf) In Interspeech, pp. 1692-1695. 2010.
8. Kégl, Balázs, and Róbert Busa-Fekete. ["Boosting products of base classifiers."](http://dl.acm.org/citation.cfm?id=1553439) In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 497-504. ACM, 2009.
9. Rosenblatt, Frank. ["The perceptron: A probabilistic model for information storage and organization in the brain."](http://psycnet.apa.org/journals/rev/65/6/386/) Psychological review 65, no. 6 (1958): 386.
10. Bishop, Christopher M. ["Pattern recognition."](http://s3.amazonaws.com/academia.edu.documents/30428242/bg0137.pdf?AWSAccessKeyId=AKIAJ56TQJRTWSMTNPEA&Expires=1484816640&Signature=85Ad6%2Fca8T82pmHzxaSXermovIA%3D&response-content-disposition=inline%3B%20filename%3DPattern_recognition_and_machine_learning.pdf) Machine Learning 128 (2006): 1-58.
10. Bishop, Christopher M. ["Pattern recognition."](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf) Machine Learning 128 (2006): 1-58.
<br/>
<arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><imgalt="知识共享许可协议"style="border-width:0"src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png"/></a><br/><spanxmlns:dct="http://purl.org/dc/terms/"href="http://purl.org/dc/dcmitype/Text"property="dct:title"rel="dct:type">This book</span> is created by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and uses <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Shared knowledge signature - non commercial use-Sharing 4.0 International Licensing Protocal</a>.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
7. Deng, Li, Michael L. Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, and Geoffrey E. Hinton. ["Binary coding of speech spectrograms using a deep auto-encoder."](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.1908&rep=rep1&type=pdf) In Interspeech, pp. 1692-1695. 2010.
8. Kégl, Balázs, and Róbert Busa-Fekete. ["Boosting products of base classifiers."](http://dl.acm.org/citation.cfm?id=1553439) In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 497-504. ACM, 2009.
9. Rosenblatt, Frank. ["The perceptron: A probabilistic model for information storage and organization in the brain."](http://psycnet.apa.org/journals/rev/65/6/386/) Psychological review 65, no. 6 (1958): 386.
10. Bishop, Christopher M. ["Pattern recognition."](http://s3.amazonaws.com/academia.edu.documents/30428242/bg0137.pdf?AWSAccessKeyId=AKIAJ56TQJRTWSMTNPEA&Expires=1484816640&Signature=85Ad6%2Fca8T82pmHzxaSXermovIA%3D&response-content-disposition=inline%3B%20filename%3DPattern_recognition_and_machine_learning.pdf) Machine Learning 128 (2006): 1-58.
10. Bishop, Christopher M. ["Pattern recognition."](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf) Machine Learning 128 (2006): 1-58.
@@ -335,10 +335,10 @@ This tutorial describes a few basic Deep Learning models viz. Softmax regression
7. Deng, Li, Michael L. Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, and Geoffrey E. Hinton. ["Binary coding of speech spectrograms using a deep auto-encoder."](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.1908&rep=rep1&type=pdf) In Interspeech, pp. 1692-1695. 2010.
8. Kégl, Balázs, and Róbert Busa-Fekete. ["Boosting products of base classifiers."](http://dl.acm.org/citation.cfm?id=1553439) In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 497-504. ACM, 2009.
9. Rosenblatt, Frank. ["The perceptron: A probabilistic model for information storage and organization in the brain."](http://psycnet.apa.org/journals/rev/65/6/386/) Psychological review 65, no. 6 (1958): 386.
10. Bishop, Christopher M. ["Pattern recognition."](http://s3.amazonaws.com/academia.edu.documents/30428242/bg0137.pdf?AWSAccessKeyId=AKIAJ56TQJRTWSMTNPEA&Expires=1484816640&Signature=85Ad6%2Fca8T82pmHzxaSXermovIA%3D&response-content-disposition=inline%3B%20filename%3DPattern_recognition_and_machine_learning.pdf) Machine Learning 128 (2006): 1-58.
10. Bishop, Christopher M. ["Pattern recognition."](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf) Machine Learning 128 (2006): 1-58.
<br/>
<arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><imgalt="知识共享许可协议"style="border-width:0"src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png"/></a><br/><spanxmlns:dct="http://purl.org/dc/terms/"href="http://purl.org/dc/dcmitype/Text"property="dct:title"rel="dct:type">This book</span> is created by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and uses <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Shared knowledge signature - non commercial use-Sharing 4.0 International Licensing Protocal</a>.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
7. Deng, Li, Michael L. Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, and Geoffrey E. Hinton. ["Binary coding of speech spectrograms using a deep auto-encoder."](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.1908&rep=rep1&type=pdf) In Interspeech, pp. 1692-1695. 2010.
8. Kégl, Balázs, and Róbert Busa-Fekete. ["Boosting products of base classifiers."](http://dl.acm.org/citation.cfm?id=1553439) In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 497-504. ACM, 2009.
9. Rosenblatt, Frank. ["The perceptron: A probabilistic model for information storage and organization in the brain."](http://psycnet.apa.org/journals/rev/65/6/386/) Psychological review 65, no. 6 (1958): 386.
10. Bishop, Christopher M. ["Pattern recognition."](http://s3.amazonaws.com/academia.edu.documents/30428242/bg0137.pdf?AWSAccessKeyId=AKIAJ56TQJRTWSMTNPEA&Expires=1484816640&Signature=85Ad6%2Fca8T82pmHzxaSXermovIA%3D&response-content-disposition=inline%3B%20filename%3DPattern_recognition_and_machine_learning.pdf) Machine Learning 128 (2006): 1-58.
10. Bishop, Christopher M. ["Pattern recognition."](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf) Machine Learning 128 (2006): 1-58.
@@ -108,4 +108,4 @@ This tutorial goes over traditional approaches in recommender system and a deep
7. Covington P, Adams J, Sargin E. [Deep neural networks for youtube recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf)[C]//Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016: 191-198.
<br/>
<arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><imgalt="Creative Commons"style="border-width:0"src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png"/></a><br/><spanxmlns:dct="http://purl.org/dc/terms/"href="http://purl.org/dc/dcmitype/Text"property="dct:title"rel="dct:type">This tutorial</span> was created by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">the PaddlePaddle community</a> and published under <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Common Creative 4.0 License</a>。
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
@@ -150,7 +150,7 @@ This tutorial goes over traditional approaches in recommender system and a deep
7. Covington P, Adams J, Sargin E. [Deep neural networks for youtube recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf)[C]//Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016: 191-198.
<br/>
<arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><imgalt="Creative Commons"style="border-width:0"src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png"/></a><br/><spanxmlns:dct="http://purl.org/dc/terms/"href="http://purl.org/dc/dcmitype/Text"property="dct:title"rel="dct:type">This tutorial</span> was created by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">the PaddlePaddle community</a> and published under <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Common Creative 4.0 License</a>。
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
@@ -490,4 +490,4 @@ In this chapter, we use sentiment analysis as an example to introduce applying d
9. Zhou J, Xu W. [End-to-end learning of semantic role labeling using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
@@ -532,7 +532,7 @@ In this chapter, we use sentiment analysis as an example to introduce applying d
9. Zhou J, Xu W. [End-to-end learning of semantic role labeling using recurrent neural networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf)[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
This tutorial is contributed by <axmlns:cc="http://creativecommons.org/ns#"href="http://book.paddlepaddle.org"property="cc:attributionName"rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <arel="license"href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.