《Model-based Deep Hand Pose Estimation》X Zhou, Q Wan, W Zhang, X Xue, Y Wei [Fudan University & MSR] (2016)O网页链接GitHub:O网页链接
《Are there any academic papers that discuss the relationship between neural network size and the complexity of the data they are able to model? | Quora》O网页链接
【免费书:强化学习介绍】《Reinforcement Learning: An Introduction》R S. Sutton, A G. Barto, MIT Press, Cambridge, MAO网页链接第一版(1998)打包:O网页链接第二版(2015草稿):O网页链接以及相关课程资料:O网页链接 "Implementing exercises" by Julian Schrittwieser GitHub:O网页链接
'metric-learn - Metric learning algorithms in Python' GitHub:Oall-umass/metric-learn · GitHub
'RLPy - Reinforment Learning Framework' GitHub:O网页链接
《LSTMetallica: Generation drum tracks by learning the drum tracks of 60 Metallica songs | Keunwoo Choi》by KEUNWOOCHOIO网页链接 GitHub:O网页链接
【论文:深度强化学习异步算法(多核单CPU高效多线程)】《Asynchronous Methods for Deep Reinforcement Learning》V Mnih, A P Badia, M Mirza, A Graves, T P. Lillicrap, T Harley, D Silver, K Kavukcuoglu [Google] (2016)O网页链接
Reproduction by Kosuke Miyoshi GitHub:O网页链接//@爱可可-爱生活: Asynchronous-Methods-for-Deep-Reinforcement-Learning by Samuel Graván GitHub:O网页链接
《CS188 Artificial Intelligence - Lecture 24 Advanced Applications: Robot Learning | YouTube》by Pieter AbbeelO网页链接
【"Max-Margin DeepWalk: Discriminative Learning of Network Representation"】#IJCAI 2016#论文Max-Margin DeepWalk: Discriminative Learning of Network Representation 已经公开：O网页链接数据代码下载：O网页链接本文由博士生涂存超与访问本科生张惟诚共同完成，提出Max-Margin DeepWalk算法，引入社会网络节点的标签信息，使网络表示学习模型具有更好的区分能力。
'Sequence-to-sequence model with LSTM encoder/decoders and attention' by harvardnlp GitHub:O网页链接
《Python 201: An Intro to itertools | The Mouse Vs. The Python》by MikeO网页链接
《Randomized Forest :Thought vectors to build a new class of Ensemble algorithms》by Ashish KumarO网页链接
《10+2 Data Science Methods that Every Data Scientist Should Know in 2016》by Takashi J. OZAKIO网页链接
《The amazing power of word vectors》by Adrian ColyerO网页链接
《What is the difference between Bagging and Boosting?》by xristicaO网页链接 Part2:O网页链接Part3:O网页链接
《Comprehensive Guide to Learning Python for Data Analysis and Data Science》by Martijn TheuwissenO网页链接
《Holding Your Hand Like a Small Child Through A Neural Network》by psingman Part1:O网页链接Part2:O网页链接
《Evolutionary Computation》by Alan Zucconi Part1:O网页链接
【论文+代码(即将):Attention-based神经(感知)机器翻译的高效实现】《Effective Approaches to Attention-based Neural Machine Translation》MT Luong, H Pham, CD Manning (EMNLP 2015)O网页链接code&models(waiting…):O网页链接
"Effective Approaches to Attention-based Neural Machine Translationsm" GitHub:O网页链接
《Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation》T D. Kulkarni, K R. Narasimhan, A Saeedi, J B. Tenenbaum [MIT] (2016)O网页链接
《Trading-Off Cost of Deployment Versus Accuracy in Learning Predictive Models》D P. Robinson, S Saria [Johns Hopkins University] (2016)O网页链接
《A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion》J Welbl, G Bouchard, S Riedel [University College London] (2016)O网页链接
'GPy - Gaussian processes framework in python' GitHub:O网页链接
人工智能学家 提供的译文《微软：让机器根据图片组描绘故事》O微软：让机器根据图片组描绘故事//@爱可可-爱生活: 《Microsoft researchers are teaching AI to write stories about groups of photos | VentureBeat》by Jordan NovetO网页链接 "Microsoft SIND - Sequential Image Narrative Dataset: first dataset for sequential vision-to-language"O网页链接ref:《Visual Storytelling》T-H Huang, F Ferraro... [MSR] (2016)O网页链接
《走近人脸检测：从 VJ 到深度学习》by 邬书哲 via:人工智能学家 上:O长文干货！走近人脸检测：从 VJ 到深度学习（上）
下：O长文干货！走近人脸检测：从VJ到深度学习（下）via:深度学习大讲堂
《Chained Gaussian Processes》A D. Saul, J Hensman, A Vehtari, N D. Lawrence [University of Sheffield] (2016)O网页链接 GitHub:O网页链接
《ParseNet: Looking Wider to See Better》W Liu, A Rabinovich, A C. Berg [UNC Chapel Hill & MagicLeap] (2015)O网页链接GitHub:O网页链接ref:O网页链接
《An Attentive Neural Architecture for Fine-grained Entity Type Classification》S Shimaoka, P Stenetorp, K Inui, S Riedel [Tohoku University & University College London] (2016)O网页链接
《A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python)》by Manish SaraswatO网页链接 @数盟社区提供的译文《基于树的建模-完整教程(R & Python)》O网页链接
