图 书 馆开课单位--斯坦福大学
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91狭义相对论
Leonard Susskind(斯坦福大学) 共8讲；简介：本课程是由六个季度组成，将探索现代物理学的基本理论基础，涉及的话题包括经典力学、量子力学、广义相对论、狭义相对论、宇宙学、电磁学和黑洞等...
热度：109
Leonard Susskind(斯坦福大学) 共8讲；简介：本课程是由六个季度组成，将探索现代物理学的基本理论基础，涉及的话题包括经典力学、量子力学、广义相对论、狭义相对论、宇宙学、电磁学和黑洞等...
热度：109
9293Lecture 2 - An Application of Supervised Learning - Autonomous Deriving [2讲的有监督学习自治得到应用]
Andrew Ng(斯坦福大学) An Application of Supervised Learning - Autonomous Deriving, ALVINN, Linear Regression, Gradient Descent, Batch Gradient Descent, Stochastic Gradient ...
热度：36
Andrew Ng(斯坦福大学) An Application of Supervised Learning - Autonomous Deriving, ALVINN, Linear Regression, Gradient Descent, Batch Gradient Descent, Stochastic Gradient ...
热度：36
94PhD Thesis Defense: Dynamics of large networks[博士论文答辩：大型网络的动态]
Jure Leskovec(斯坦福大学) A basic premise behind the study of large networks is that interaction leads to complex collective behavior. In our work we found very interesting and...
热度：43
Jure Leskovec(斯坦福大学) A basic premise behind the study of large networks is that interaction leads to complex collective behavior. In our work we found very interesting and...
热度：43
95An Overview of Compressed Sensing and Sparse Signal Recovery via L1 Minimization[压缩传感，通过L1最小化稀疏信号恢复概述]
Emmanuel Candes(斯坦福大学) In many applications, one often has fewer equations than unknowns. While this seems hopeless, the premise that the object we wish to recover is sparse...
热度：52
Emmanuel Candes(斯坦福大学) In many applications, one often has fewer equations than unknowns. While this seems hopeless, the premise that the object we wish to recover is sparse...
热度：52
96Matrix Completion via Convex Optimization: Theory and Algorithms[基于凸优化的矩阵完成：理论与算法]
Emmanuel Candes(斯坦福大学) This talk considers a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. In partially filled ou...
热度：42
Emmanuel Candes(斯坦福大学) This talk considers a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. In partially filled ou...
热度：42
97Correcting for Missing Data in Information Cascades[校正的信息级联数据丢失]
Jure Leskovec(斯坦福大学) Transmission of infectious diseases, propagation of information, and spread of ideas and influence through social networks are all examples of diffusi...
热度：56
Jure Leskovec(斯坦福大学) Transmission of infectious diseases, propagation of information, and spread of ideas and influence through social networks are all examples of diffusi...
热度：56
98Networks, communities and the ground-truth [网络，社区和地面的真理]
Jure Leskovec(斯坦福大学) The Web, society, information, cells and brain can all be represented and studied as complex networks of interactions. Nodes in such networks tend to ...
热度：105
Jure Leskovec(斯坦福大学) The Web, society, information, cells and brain can all be represented and studied as complex networks of interactions. Nodes in such networks tend to ...
热度：105
99Lecture 17 - Generalization to Continuous States[讲座17：泛化到连续状态]
Andrew Ng(斯坦福大学) Generalization to Continuous States, Discretization & Curse of Dimensionality, Models/Simulators, Fitted Value Iteration, Finding Optimal Policy
热度：59
Andrew Ng(斯坦福大学) Generalization to Continuous States, Discretization & Curse of Dimensionality, Models/Simulators, Fitted Value Iteration, Finding Optimal Policy
热度：59
100Supporting Collaborative Ontology Development in Protege[支持Protege中的协同本体开发]
Tania Tudorache(斯坦福大学) Ontologies are becoming so large in their coverage that no single person or a small group of people can develop them effectively and ontology developm...
热度：142
Tania Tudorache(斯坦福大学) Ontologies are becoming so large in their coverage that no single person or a small group of people can develop them effectively and ontology developm...
热度：142