diff --git a/README.md b/README.md
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+++ b/README.md
@@ -33,47 +33,50 @@ Due to github Large file storage limition issue, all books pdf stored in gitlab
 
 ### Mathematics
 
- - [A First Course in ProbabilityA First Course in Probability (8th)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/A%20First%20Course%20in%20Probability,%208th%20-%20Sheldon%20M%20Ross.pdf) - Sheldon M Ross
- - [Convex Optimization](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Convex%20Optimization%20-%20Stephen%20Boyd.pdf) - Stephen Boyd
- - [Elements of Information Theory Elements](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Elements%20of%20Information%20Theory%20Elements%20-%20Thomas%20Cover%20&%20Jay%20A%20Thomas.pdf) - Thomas Cover & Jay A Thomas
- - [Introduction to Linear Algebra (5th)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Introduction%20to%20Linear%20Algebra%205th%20-%20Gilbert%20Strang.pdf) - Gilbert Strang
- - [Linear Algebra and Its Applications (5th)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Linear%20Algebra%20and%20Its%20Applications%205th%20-%20David%20C%20Lay.pdf) - David C Lay
- - [Probability Theory The Logic of Science](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Probability%20Theory%20The%20Logic%20of%20Science%20-%20Edwin%20Thompson%20Jaynes.pdf) - Edwin Thompson Jaynes
- - [Statistical Inference (2nd)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Statistical%20Inference%202nd%20-%20Roger%20Casella.pdf) - Roger Casella
- - [信息论基础 (原书Elements of Information Theory Elements第2版)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E4%BF%A1%E6%81%AF%E8%AE%BA%E5%9F%BA%E7%A1%80%20%20%E5%8E%9F%E4%B9%A6%E7%AC%AC2%E7%89%88%20-%20Thomas%20Cover%20&%20Jay%20A%20Thomas.pdf) - Thomas Cover & Jay A Thomas
- - [凸优化 (原书Convex Optimization)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E5%87%B8%E4%BC%98%E5%8C%96%20-%20Stephen%20Boyd.pdf) - Stephen Boyd
- - [数理统计学教程](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E6%95%B0%E7%90%86%E7%BB%9F%E8%AE%A1%E5%AD%A6%E6%95%99%E7%A8%8B%20-%20%E9%99%88%E5%B8%8C%E5%84%92.pdf) - 陈希儒
- - [概率论基础教程 (原书A First Course in ProbabilityA First Course in Probability第9版)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E6%A6%82%E7%8E%87%E8%AE%BA%E5%9F%BA%E7%A1%80%E6%95%99%E7%A8%8B%20(%E5%8E%9F%E4%B9%A6%E7%AC%AC9%E7%89%88)%20-%20Sheldon%20M%20Ross.pdf) - Sheldon M Ross
- - [线性代数及其应用 (原书Linear Algebra and Its Applications第3版)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0%E5%8F%8A%E5%85%B6%E5%BA%94%E7%94%A8%20(%E5%8E%9F%E4%B9%A6%E7%AC%AC3%E7%89%88)%20-%20David%20C%20Lay.pdf) - David C Lay
- - [统计推断 (原书Statistical Inference第二版)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E7%BB%9F%E8%AE%A1%E6%8E%A8%E6%96%AD%20(%E5%8E%9F%E4%B9%A6%E7%AC%AC%E4%BA%8C%E7%89%88)%20-%20Roger%20Casella.pdf) - Roger Casella
+- [A First Course in ProbabilityA First Course in Probability (8th)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/A%20First%20Course%20in%20Probability,%208th%20-%20Sheldon%20M%20Ross.pdf) - Sheldon M Ross
+- [Convex Optimization](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Convex%20Optimization%20-%20Stephen%20Boyd.pdf) - Stephen Boyd
+- [Elements of Information Theory Elements](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Elements%20of%20Information%20Theory%20Elements%20-%20Thomas%20Cover%20&%20Jay%20A%20Thomas.pdf) - Thomas Cover & Jay A Thomas
+- [Introduction to Linear Algebra (5th)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Introduction%20to%20Linear%20Algebra%205th%20-%20Gilbert%20Strang.pdf) - Gilbert Strang
+- [Linear Algebra and Its Applications (5th)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Linear%20Algebra%20and%20Its%20Applications%205th%20-%20David%20C%20Lay.pdf) - David C Lay
+- [Probability Theory The Logic of Science](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Probability%20Theory%20The%20Logic%20of%20Science%20-%20Edwin%20Thompson%20Jaynes.pdf) - Edwin Thompson Jaynes
+- [Statistical Inference (2nd)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/Statistical%20Inference%202nd%20-%20Roger%20Casella.pdf) - Roger Casella
+- [信息论基础 (原书Elements of Information Theory Elements第2版)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E4%BF%A1%E6%81%AF%E8%AE%BA%E5%9F%BA%E7%A1%80%20%20%E5%8E%9F%E4%B9%A6%E7%AC%AC2%E7%89%88%20-%20Thomas%20Cover%20&%20Jay%20A%20Thomas.pdf) - Thomas Cover & Jay A Thomas
+- [凸优化 (原书Convex Optimization)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E5%87%B8%E4%BC%98%E5%8C%96%20-%20Stephen%20Boyd.pdf) - Stephen Boyd
+- [数理统计学教程](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E6%95%B0%E7%90%86%E7%BB%9F%E8%AE%A1%E5%AD%A6%E6%95%99%E7%A8%8B%20-%20%E9%99%88%E5%B8%8C%E5%84%92.pdf) - 陈希儒
+- [概率论基础教程 (原书A First Course in ProbabilityA First Course in Probability第9版)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E6%A6%82%E7%8E%87%E8%AE%BA%E5%9F%BA%E7%A1%80%E6%95%99%E7%A8%8B%20(%E5%8E%9F%E4%B9%A6%E7%AC%AC9%E7%89%88)%20-%20Sheldon%20M%20Ross.pdf) - Sheldon M Ross
+- [线性代数及其应用 (原书Linear Algebra and Its Applications第3版)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0%E5%8F%8A%E5%85%B6%E5%BA%94%E7%94%A8%20(%E5%8E%9F%E4%B9%A6%E7%AC%AC3%E7%89%88)%20-%20David%20C%20Lay.pdf) - David C Lay
+- [统计推断 (原书Statistical Inference第二版)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Mathematics/%E7%BB%9F%E8%AE%A1%E6%8E%A8%E6%96%AD%20(%E5%8E%9F%E4%B9%A6%E7%AC%AC%E4%BA%8C%E7%89%88)%20-%20Roger%20Casella.pdf) - Roger Casella
 
 ### Machine Learning
 
- - [Information Theory, Inference and Learning Algorithms](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/Information%20Theory,%20Inference%20and%20Learning%20Algorithms%20-%20David%20J%20C%20MacKay.pdf) - David J C MacKay
- - [Machine Learning](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/Machine%20Learning%20-%20Tom%20M.%20Mitchell.pdf) - Tom M. Mitchell
- - [Pattern Recognition and Machine Learning](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/Pattern%20Recognition%20and%20Machine%20Learning%20-%20Christopher%20Bishop.pdf) - Christopher Bishop
- - [The Elements of Statistical Learning](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/The%20Elements%20of%20Statistical%20Learning%20-%20Trevor%20Hastie%20.pdf) - Trevor Hastie
- - [机器学习](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E3%80%94%E4%B8%AD%E6%96%87%E7%89%88%E3%80%95%20-%20%E5%91%A8%E5%BF%97%E5%8D%8E.pdf) - 周志华
- - [机器学习 (原书Machine Learning)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E3%80%94%E4%B8%AD%E6%96%87%E7%89%88%E3%80%95-%20Tom%20M.%20Mitchell.pdf) - Tom M. Mitchell
- - [统计学习方法](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95%20-%20%E6%9D%8E%E8%88%AA.pdf) - 李航
+- [Information Theory, Inference and Learning Algorithms](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/Information%20Theory,%20Inference%20and%20Learning%20Algorithms%20-%20David%20J%20C%20MacKay.pdf) - David J C MacKay
+- [Machine Learning](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/Machine%20Learning%20-%20Tom%20M.%20Mitchell.pdf) - Tom M. Mitchell
+- [Pattern Recognition and Machine Learning](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/Pattern%20Recognition%20and%20Machine%20Learning%20-%20Christopher%20Bishop.pdf) - Christopher Bishop
+- [The Elements of Statistical Learning](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/The%20Elements%20of%20Statistical%20Learning%20-%20Trevor%20Hastie%20.pdf) - Trevor Hastie
+- [机器学习](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E3%80%94%E4%B8%AD%E6%96%87%E7%89%88%E3%80%95%20-%20%E5%91%A8%E5%BF%97%E5%8D%8E.pdf) - 周志华
+- [机器学习 (原书Machine Learning)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E3%80%94%E4%B8%AD%E6%96%87%E7%89%88%E3%80%95-%20Tom%20M.%20Mitchell.pdf) - Tom M. Mitchell
+- [统计学习方法](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Machine%20Learning/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95%20-%20%E6%9D%8E%E8%88%AA.pdf) - 李航
 
 ### Deep Learning
 
- - [Deep Learning](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Deep%20Learning%20-%20Ian%20Goodfellow%20&%20Yoshua%20Bengio%20&%20Aaron%20Courville.pdf) - Ian Goodfellow & Yoshua Bengio & Aaron Courville
- - [Deep Learning Methods and Applications](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Deep%20Learning%20Methods%20and%20Applications%20-%20Li%20Deng%20&%20Dong%20Yu.pdf) - Li Deng & Dong Yu
- - [Learning Deep Architectures for AI](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Learning%20Deep%20Architectures%20for%20AI%20-%20Yoshua%20Bengio.pdf) - Yoshua Bengio
- - [Machine Learning An Algorithmic Perspective (2nd)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Machine%20Learning%20An%20Algorithmic%20Perspective%202nd%20-%20Stephen%20Marsland.pdf) - Stephen Marsland
- - [Neural Network Design (2nd)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Neural%20Network%20Design%202nd%20-%20Martin%20Hagan.pdf) - Martin Hagan
- - [Neural Networks and Learning Machines (3rd)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Neural%20Networks%20and%20Learning%20Machines%203rd%20-%20Simon%20Haykin.pdf) - Simon Haykin
- - [Neural Networks for Applied Sciences and Engineering](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Neural%20Networks%20for%20Applied%20Sciences%20and%20Engineering%20-%20Sandhya%20Samarasinghe.pdf) - Sandhya Samarasinghe
- - [深度学习 (原书Deep Learning)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%20-%20Ian%20Goodfellow%20&%20Yoshua%20Bengio%20&%20Aaron%20Courville.pdf) - Ian Goodfellow & Yoshua Bengio & Aaron Courville
- - [神经网络与机器学习 (原书Neural Networks and Learning Machines)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E4%B8%8E%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%20-%20Simon%20Haykin.pdf) - Simon Haykin
- - [神经网络设计 (原书Neural Network Design)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E8%AE%BE%E8%AE%A1%20-%20Martin%20Hagan.pdf) - Martin Hagan
+- [Deep Learning](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Deep%20Learning%20-%20Ian%20Goodfellow%20&%20Yoshua%20Bengio%20&%20Aaron%20Courville.pdf) - Ian Goodfellow & Yoshua Bengio & Aaron Courville
+- [Deep Learning Methods and Applications](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Deep%20Learning%20Methods%20and%20Applications%20-%20Li%20Deng%20&%20Dong%20Yu.pdf) - Li Deng & Dong Yu
+- [Learning Deep Architectures for AI](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Learning%20Deep%20Architectures%20for%20AI%20-%20Yoshua%20Bengio.pdf) - Yoshua Bengio
+- [Machine Learning An Algorithmic Perspective (2nd)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Machine%20Learning%20An%20Algorithmic%20Perspective%202nd%20-%20Stephen%20Marsland.pdf) - Stephen Marsland
+- [Neural Network Design (2nd)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Neural%20Network%20Design%202nd%20-%20Martin%20Hagan.pdf) - Martin Hagan
+- [Neural Networks and Learning Machines (3rd)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Neural%20Networks%20and%20Learning%20Machines%203rd%20-%20Simon%20Haykin.pdf) - Simon Haykin
+- [Neural Networks for Applied Sciences and Engineering](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/Neural%20Networks%20for%20Applied%20Sciences%20and%20Engineering%20-%20Sandhya%20Samarasinghe.pdf) - Sandhya Samarasinghe
+- [深度学习 (原书Deep Learning)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%20-%20Ian%20Goodfellow%20&%20Yoshua%20Bengio%20&%20Aaron%20Courville.pdf) - Ian Goodfellow & Yoshua Bengio & Aaron Courville
+- [神经网络与机器学习 (原书Neural Networks and Learning Machines)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E4%B8%8E%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%20-%20Simon%20Haykin.pdf) - Simon Haykin
+- [神经网络设计 (原书Neural Network Design)](https://gitlab.com/zslucky/awesome-AI-books/raw/master/Deep%20Learning/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E8%AE%BE%E8%AE%A1%20-%20Martin%20Hagan.pdf) - Martin Hagan
 
 ## Libs With Online Books
-
- - [Xgboost](https://xgboost.readthedocs.io/en/latest/tutorials/model.html) - Xgboost lib's document.
- - [LightGBM](https://lightgbm.readthedocs.io/en/latest/Features.html#) - Microsoft lightGBM lib's features document.
- - [CatBoost](https://arxiv.org/pdf/1706.09516.pdf) - Yandex Catboost lib's key algorithm pdf papper.
- - [StackNet](https://github.com/kaz-Anova/StackNet) - Some model stacking algorithms implemented in this lib.
- - [TPOT](https://github.com/EpistasisLab/tpot) - TPOT is a lib for AutoML.
+- Machine Learning
+  - [Xgboost](https://xgboost.readthedocs.io/en/latest/tutorials/model.html) - Xgboost lib's document.
+  - [LightGBM](https://lightgbm.readthedocs.io/en/latest/Features.html#) - Microsoft lightGBM lib's features document.
+  - [CatBoost](https://arxiv.org/pdf/1706.09516.pdf) - Yandex Catboost lib's key algorithm pdf papper.
+  - [StackNet](https://github.com/kaz-Anova/StackNet) - Some model stacking algorithms implemented in this lib.
+  - [RGF](https://arxiv.org/pdf/1109.0887.pdf) - Learning Nonlinear Functions Using `Regularized Greedy Forest` (multi-core implementation [FastRGF](https://github.com/RGF-team/rgf/tree/master/FastRGF))
+  - [FM](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf), [FastFM](https://arxiv.org/pdf/1505.00641.pdf), [FFM](https://arxiv.org/pdf/1701.04099.pdf) - Factorization Machines and some extended algorithms
+- Auto ML
+  - [TPOT](https://github.com/EpistasisLab/tpot) - TPOT is a lib for AutoML.