7-28 7-29
【开源:Scikit-Learn兼容的(Python)半监督学习框架】"Semi-supervised learning frameworks for Python" GitHub:O网页链接
【蒙特卡罗方法介绍】《Introduction To Monte Carlo Methods》O网页链接 参阅《蒙特卡罗方法入门》O网页链接
【大脑 vs. 深度学习 Part I:计算复杂度】《The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near》O网页链接
【论文:个性化网络搜索中对相关性标签赋权的优化框架】《An Optimization Framework for Weighting Implicit Relevance Labels for Personalized Web Search》Y Ustinovskiy, G Gusev, P Serdyukov (WWW2015)O网页链接via:OWWW 2015：一个神奇的会议
【幻灯:(D3.js)高效网络数据可视化指南】《Creating Effective Network Data Visualization》O网页链接
【论文:利用多臂老虎机模型从产品搜索排序中收集额外反馈信息】《Gathering Additional Feedback on Search Results by Multi-Armed Bandits with Respect to Production Ranking》A Vorobev, D Lefortier, G Gusev (WWW2015)O网页链接via:OWWW 2015：一个神奇的会议
【Jeffrey De Fauw的Kaggle diabetic retinopathy竞赛参赛记录&体会&代码】《Detecting diabetic retinopathy in eye images》O网页链接
【(Python)13行代码写神经网络(Part2 optimizing SGD)】《A Neural Network in 13 lines of Python (Part 2) - Improving our neural network by optimizing Stochastic Gradient Descent》O网页链接Part1:O网页链接
【R/Python数据操作基础】《Data manipulation primitives in R and Python》O网页链接
【(JavaScript)基于图论的简单推荐引擎】《Using Graph Theory to Build a Simple Recommendation Engine in JavaScript - Leveraging User Behavior to Drive Recommendations》O网页链接GitHub:O网页链接
【Kaggle's CrowdFlower搜索结果相关性竞赛第一名访谈】《CrowdFlower Winner's Interview: 1st place, Chenglong Chen》O网页链接
【机器学习的可视化介绍(Part1)】《A Visual Introduction to Machine Learning》by R2D3O网页链接
【Chainer下各种优化算法(SGD/AdaGrad/RMSprop/ADAM/...)比较】《Chainer Optimizer Comparison》O网页链接
【论文:Ladder Network半监督学习】《Semi-Supervised Learning with Ladder Network》A Rasmus, H Valpola, M Honkala, M Berglund, T Raiko (2015) MNIST上100标注样本训练达到1.13%错误率O网页链接 Theano/Blocks实现代码:O网页链接
【(Python)高维数据散点图(Corner plot)绘制工具triangle.py】GitHub:O网页链接
【深度学习对抗样本的误解与事实】《Deep Learning Adversarial Examples – Clarifying Misconceptions》by Ian Goodfellow [Google]O网页链接 提供的相关文章《深度学习之对抗样本问题》O网页链接//@爱可可-爱生活:@CSDN云计算提供的译文《深度学习对抗样本的八个误解与事实》O网页链接
【"What's wrong with convolutional neural network" & "Whats wrong with deep learning"】分享两个有意思的视频：一个是Geoffrey Hinton去年在MIT的talk：What's wrong with convolutional neural network;另一个是杨立昆(Yann Lecun)在今年CVPR上的talk：Whats wrong with deep learning. 百度网盘：O网页链接
【"Feature engineering is a lot like oxygen. You can't do without it, but you rarely give it much thought"】【免费书:数据科学生存指南】《The Field Guide to Data Science》by Booz Allen Hamilton "Understanding the DNA of Data Science"O网页链接云:O网页链接
【论文:异步随机优化算法扰动迭代分析】《Perturbed Iterate Analysis for Asynchronous Stochastic Optimization》H Mania, X Pan, D Papailiopoulos, B Recht, K Ramchandran, M Jordan (2015)O网页链接
【论文+视频+Slide+代码(VM):基于机器学习的代码自动反编译】《BYTEWEIGHT: Learning to Recognize Functions in Binary Code》O网页链接代码:O网页链接
【论文:基于文本的音乐自动生成】《Generating Music from Literature》H Davis, S Mohammad (2015)O网页链接Demo:O网页链接《爱可可老师今日视野(15.07.28)》（ 分享自@简书）O网页链接
【大脑 vs. 深度学习 Part I:计算复杂度】《The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near》O网页链接 此文在 redditO网页链接上引起了热烈的讨论。上月一篇长文O网页链接的作者 jcannell 持相反观点，但尚未和本文作者 timdettmers 直接辩论。
【关联规则详解】《What the heck are Association Rules in Analytics?》O网页链接
【Nick Mills首次参加DrivenData数据科学竞赛心得分享】《What I learned from my first data science competition》O网页链接
【"Natural Language Understanding and Prediction Technologies" by Nicolae Duta, IJCAI 2015 TutorialT17】: The Evolution of Natural Language Understanding and Prediction Technologies: from Formal Grammars to Large Scale Machine Learning - Nicolae Duta ijcai-15 tutorial.O网页链接
【基于Spark/MLlib/GraphX的大规模高效机器学习(LR/LDA/FM/DNN/...)平台Zen】GitHub:O网页链接
【论文:基于中小型计算集群的大规模主题模型LightLDA】《LightLDA: Big Topic Models on Modest Computer Clusters》J Yuan, F Gao, Q Ho, W Dai, J Wei, X Zheng, E Xing, T Liu, W Ma (WWW2015)O网页链接
【论文:面向非凸优化的递归分解(IJCAI15杰出论文)】《Recursive Decomposition for Nonconvex Optimization》 A Friesen, P Domingos (IJCAI2015)O网页链接
【Ilya Kavalerov的Kaggle diabetic retinopathy竞赛首次参赛体会(ConvNet)】《My 1st Kaggle ConvNet: Getting to 3rd Percentile in 3 months》O网页链接
【IPython notebook教程】《Efficient Data Analysis with the IPython Notebook》 GitHub:O网页链接
【arXiv+Github+Links+Discussion跟踪论文开源实现的协同列表网站GitXiv】O网页链接《GitXiv — Collaborative Open Computer Science》O网页链接
【Kaggle's CrowdFlower搜索结果相关性竞赛第一名访谈】《CrowdFlower Winner's Interview: 1st place, Chenglong Chen》O网页链接 转一发吧。为了ensemble，前期花了很多时间在代码重构方面，慢慢分离出来preprocessing，feature extraction，model building，model evaluation这个pipeline，这个是挺有帮助的。
【论文+代码(Java):基于规则的非特定领域事件抽取框架】《A Domain-independent Rule-based Framework for Event Extraction》MAVEG Hahn, PTHM Surdeanu (ACL2015)O网页链接Code:O网页链接Docs:O网页链接
【诗歌音律拓扑(sonic topology)可视化工具Poemage】O网页链接Paper:O网页链接Code:O网页链接
【高效的Python数据分析框架Ibis】O网页链接GitHub:O网页链接通过IPN了解Ibis:O网页链接Slide:《Ibis: Scaling the Python Data Experience》O网页链接云:O网页链接
【论文+代码:面向网络级规模的并行流标记EM-tree聚类算法】《Parallel Streaming Signature EM-tree: A Clustering Algorithm for Web Scale Applications》C Vries, L Vine, S Geva (WWW2015)O网页链接LMW-tree:O网页链接GitHub:O网页链接【幻灯:(nVIDIA深度学习课程)GPU深度学习介绍】《Introduction To Deep Learning With GPUs》O网页链接云:O网页链接
【免费书:机器学习资源精选汇编】《The Machine Learning Salon Starter Kit》by Jacqueline Isabelle ForienO网页链接云:O网页链接
【可重现数据驱动研究平台REP】全称是Reproducible Experiment Platform，统一封装TMVA, Sklearn, XGBoost, Uboost等分类实现，进行大数据集共享一致性对比试验，可在集群上完成并行训练 GitHub:O网页链接REP(Reproducible Experiment Platform)文档:O网页链接
【狄利克雷分布/狄利克雷过程笔记】《Notes on the Dirichlet Distribution and Dirichlet Process》O网页链接ipn:O网页链接
