<commit_before>---
layout:     post
title:      "Learning"
subtitle:   " \"学习资料\""
date:       2016-06-30
author:     "LiGuang"
header-img: "img/post-bg-2015.jpg"
catalog: true
tags:
    - 学习
---

> “Yeah It's on. ”


## 前言

记录一些学习资料


<p id = "build"></p>
---

## 正文

1. [Awesome Random Forest](https://github.com/kjw0612/awesome-random-forest#theory)

2. [随机森林](http://www.cnblogs.com/maybe2030/p/4585705.html)

3. [Learning RNN from scratch (RNN神经网络参数推导)](http://blog.csdn.net/watkinsong/article/details/51773524)

4. [Deep Learning for Recommender Systems - YouTube](https://www.youtube.com/watch?v=KZ7bcfYGuxw)

5. [Probabilistic machine learning and artificial intelligence](http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html)

6. [Nature论文详解概率机器学习：从不确定性表征到自动建模](https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&mid=2650716475&idx=1&sn=2b03deead0c1e63be80fdc239293e805)

7. [反向传播算法可视化](http://neupy.com/2015/07/04/visualize_backpropagation_algorithms.html)

8. Generative Moment Matching Networks:生成式矩匹配网络(GMMNs) [Paper](http://jmlr.org/proceedings/papers/v37/li15.html), [Github](https://github.com/yujiali/gmmn)

9. A Neural Autoregressive Approach to Collaborative Filtering [Paper](https://arxiv.org/abs/1605.09477)，[Github](https://github.com/Ian09/CF-NADE)

10. [CNN原理图解](http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/)

11. [Factorization Machines](http://www.algo.uni-konstanz.de/members/rendle/pdf/Rendle2010FM.pdf), [FFM原理与实践](http://tech.meituan.com/deep-understanding-of-ffm-principles-and-practices.html)

12. [Minimizing Regret](http://www.minimizingregret.com/)(优化方面)

13. [RNN&&LSTM资料](http://suanfazu.com/t/rnn-lstm/13587)

14. [PRML网页版](https://www.gitbook.com/book/mqshen/prml/details)

15. [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](http://arxiv.org/abs/1511.05298), [简介](https://mp.weixin.qq.com/s?__biz=MzIxNzE2MTM4OA==&mid=2665642872&idx=1&sn=810d2a39c474faa37fe857d4513100ac)。

16. [Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units](https://arxiv.org/abs/1606.08415v1) 

17. 大脑学习与机器学习 [Part.1 Temporal Differences](http://blog.shakirm.com/2016/02/learning-in-brains-and-machines-1/) , [Part.2 The Dogma of Sparsity](http://blog.shakirm.com/2016/04/learning-in-brains-and-machines-2/), [Part.3 Synergistic and Modular Action ](http://blog.shakirm.com/2016/07/learning-in-brains-and-machines-3-synergistic-and-modular-action/)

18. [Predict Sentiment From Movie Reviews Using Deep Learning](http://machinelearningmastery.com/predict-sentiment-movie-reviews-using-deep-learning/):使用深度学习从电影评论中预测情绪

19. [Model-Free Prediction and Control](http://kvfrans.com/model-free-prediction-and-control/)：无模型预测与控制。

20. 深度学习与自然语言处理(斯坦福cs224d)：[Lecture1](http://blog.csdn.net/longxinchen_ml/article/details/51567960), [Lecture2](http://blog.csdn.net/longxinchen_ml/article/details/51648532)， [Lecture3](http://blog.csdn.net/longxinchen_ml/article/details/51711172)， [大作业检测与解答1](http://blog.csdn.net/han_xiaoyang/article/details/51760923)， [大作业检测与解答2](http://blog.csdn.net/han_xiaoyang/article/details/51815683)。

21. [Machine learning techniques for price change forecast using the limit order book data](http://jcyhong.github.io/assets/machine-learning-price-movements.pdf)

22. [Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model](http://arxiv.org/abs/1607.00067) 

23. [One-Shot Session Recommendation Systems with Combinatorial Items](http://arxiv.org/abs/1607.01381)

24. [Stochastic Portfolio Theory: A Machine Learning Perspective](http://arxiv.org/abs/1605.02654)

25. [Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization](http://arxiv.org/abs/1607.01231)

26. [Temporal Topic Analysis with Endogenous and Exogenous Processes](http://arxiv.org/abs/1607.01274)

↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓

Tensor系列

1. [New Ranks for Even-Order Tensors and Their Applications in Low-Rank Tensor Optimization](http://www.menet.umn.edu/~zhangs/Reports/2015_JMZ.pdf)

2. [Pairwise Interaction Tensor Factorization for Personalized Tag Recommendation](http://www.wsdm-conference.org/2010/proceedings/docs/p81.pdf)

3. [Tensor Principal Component Analysis via Convex Optimization](https://arxiv.org/abs/1212.2702)

4. [Tensor Decompositions and Applications](http://epubs.siam.org/doi/abs/10.1137/07070111X?journalCode=siread)

↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑



↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓

卡尔曼贝叶斯过滤器

1. [An Introduction to the Kalman Filter](http://www.cs.unc.edu/~tracker/media/pdf/SIGGRAPH2001_CoursePack_08.pdf)

2. [Kalman and Bayesian Filters in Python](http://nbviewer.jupyter.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb), [PDF](http://pan.baidu.com/s/1i34bvhF), [github](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python).

↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑


## 后记

持续更新……<commit_msg>Update 2016-06-30-Learning.markdown<commit_after>---
layout:     post
title:      "Learning"
subtitle:   " \"学习资料\""
date:       2016-06-30
author:     "LiGuang"
header-img: "img/post-bg-2015.jpg"
catalog: true
tags:
    - 学习
---

> “Yeah It's on. ”


## 前言

记录一些学习资料


<p id = "build"></p>
---

## 正文

1. [Awesome Random Forest](https://github.com/kjw0612/awesome-random-forest#theory)

2. [随机森林](http://www.cnblogs.com/maybe2030/p/4585705.html)

3. [Learning RNN from scratch (RNN神经网络参数推导)](http://blog.csdn.net/watkinsong/article/details/51773524)

4. [Deep Learning for Recommender Systems - YouTube](https://www.youtube.com/watch?v=KZ7bcfYGuxw)

5. [Probabilistic machine learning and artificial intelligence](http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html)

6. [Nature论文详解概率机器学习：从不确定性表征到自动建模](https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&mid=2650716475&idx=1&sn=2b03deead0c1e63be80fdc239293e805)

7. [反向传播算法可视化](http://neupy.com/2015/07/04/visualize_backpropagation_algorithms.html)

8. Generative Moment Matching Networks:生成式矩匹配网络(GMMNs) [Paper](http://jmlr.org/proceedings/papers/v37/li15.html), [Github](https://github.com/yujiali/gmmn)

9. A Neural Autoregressive Approach to Collaborative Filtering [Paper](https://arxiv.org/abs/1605.09477)，[Github](https://github.com/Ian09/CF-NADE)

10. [CNN原理图解](http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/)

11. [Factorization Machines](http://www.algo.uni-konstanz.de/members/rendle/pdf/Rendle2010FM.pdf), [FFM原理与实践](http://tech.meituan.com/deep-understanding-of-ffm-principles-and-practices.html)

12. [Minimizing Regret](http://www.minimizingregret.com/)(优化方面)

13. [RNN&&LSTM资料](http://suanfazu.com/t/rnn-lstm/13587)

14. [PRML网页版](https://www.gitbook.com/book/mqshen/prml/details)

15. [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](http://arxiv.org/abs/1511.05298), [简介](https://mp.weixin.qq.com/s?__biz=MzIxNzE2MTM4OA==&mid=2665642872&idx=1&sn=810d2a39c474faa37fe857d4513100ac)。

16. [Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units](https://arxiv.org/abs/1606.08415v1) 

17. 大脑学习与机器学习 [Part.1 Temporal Differences](http://blog.shakirm.com/2016/02/learning-in-brains-and-machines-1/) , [Part.2 The Dogma of Sparsity](http://blog.shakirm.com/2016/04/learning-in-brains-and-machines-2/), [Part.3 Synergistic and Modular Action ](http://blog.shakirm.com/2016/07/learning-in-brains-and-machines-3-synergistic-and-modular-action/)

18. [Predict Sentiment From Movie Reviews Using Deep Learning](http://machinelearningmastery.com/predict-sentiment-movie-reviews-using-deep-learning/):使用深度学习从电影评论中预测情绪

19. [Model-Free Prediction and Control](http://kvfrans.com/model-free-prediction-and-control/)：无模型预测与控制。

20. 深度学习与自然语言处理(斯坦福cs224d)：[Lecture1](http://blog.csdn.net/longxinchen_ml/article/details/51567960), [Lecture2](http://blog.csdn.net/longxinchen_ml/article/details/51648532)， [Lecture3](http://blog.csdn.net/longxinchen_ml/article/details/51711172)， [大作业检测与解答1](http://blog.csdn.net/han_xiaoyang/article/details/51760923)， [大作业检测与解答2](http://blog.csdn.net/han_xiaoyang/article/details/51815683)。

21. [Machine learning techniques for price change forecast using the limit order book data](http://jcyhong.github.io/assets/machine-learning-price-movements.pdf)

22. [Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model](http://arxiv.org/abs/1607.00067) 

23. [One-Shot Session Recommendation Systems with Combinatorial Items](http://arxiv.org/abs/1607.01381)

24. [Stochastic Portfolio Theory: A Machine Learning Perspective](http://arxiv.org/abs/1605.02654)

25. [Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization](http://arxiv.org/abs/1607.01231)

26. [Temporal Topic Analysis with Endogenous and Exogenous Processes](http://arxiv.org/abs/1607.01274)

---------------------------------------------------------------------------------------------------------------

Tensor系列

1. [New Ranks for Even-Order Tensors and Their Applications in Low-Rank Tensor Optimization](http://www.menet.umn.edu/~zhangs/Reports/2015_JMZ.pdf)

2. [Pairwise Interaction Tensor Factorization for Personalized Tag Recommendation](http://www.wsdm-conference.org/2010/proceedings/docs/p81.pdf)

3. [Tensor Principal Component Analysis via Convex Optimization](https://arxiv.org/abs/1212.2702)

4. [Tensor Decompositions and Applications](http://epubs.siam.org/doi/abs/10.1137/07070111X?journalCode=siread)

---------------------------------------------------------------------------------------------------------------

卡尔曼贝叶斯过滤器

1. [An Introduction to the Kalman Filter](http://www.cs.unc.edu/~tracker/media/pdf/SIGGRAPH2001_CoursePack_08.pdf)

2. [Kalman and Bayesian Filters in Python](http://nbviewer.jupyter.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb), [PDF](http://pan.baidu.com/s/1i34bvhF), [github](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python).

---------------------------------------------------------------------------------------------------------------

## 后记

持续更新……
{'fraction_non_alphanumeric': 0.21988876102800153, 'fraction_numerical': 0.07307249712313003, 'mean_word_length': 4.488947368421052, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 9, 'https://': 22, 'lorem ipsum': 0, 'www.': 18, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1990326', 'n_tokens_mistral': 4949, 'n_tokens_neox': 4922, 'n_words': 512}