图 书 馆开课单位--斯坦福大学
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81Recent Advances in Large Linear Classification[大型线性分类的最新进展]
Chih-Jen Lin(斯坦福大学) Linear classification is a useful tool in machine learning and data mining. For some data in a rich dimensional space, the prediction performance of l...
热度：29
Chih-Jen Lin(斯坦福大学) Linear classification is a useful tool in machine learning and data mining. For some data in a rich dimensional space, the prediction performance of l...
热度：29
82Lecture 3: Logistics[第三讲物流]
Stephen P. Boyd(斯坦福大学) Sorry, it’s already there. Okay, it wasn’t there two minutes ago, but anyway. So there’s a – there’s going to be a FAQ s...
热度：26
Stephen P. Boyd(斯坦福大学) Sorry, it’s already there. Okay, it wasn’t there two minutes ago, but anyway. So there’s a – there’s going to be a FAQ s...
热度：26
83Lecture 3: Summary Of Previous Lecture (Analyzing General Periodic Phenomena As A Sum Of Simple Periodic Phenomena)[第三讲：上节课的总结（把一般的周期现象分析为简单周期现象的总和）]
Brad G. Osgood(斯坦福大学) So last time, I say we took the first step in analyzing general periodic phenomena via the sum, so several combination, a linear combination of simple...
热度：32
Brad G. Osgood(斯坦福大学) So last time, I say we took the first step in analyzing general periodic phenomena via the sum, so several combination, a linear combination of simple...
热度：32
84Lecture 1: Previous Knowledge Recommended (Matlab)[第1讲：之前的知识推荐（Matlab）]
Brad G. Osgood(斯坦福大学) We are on the air. Okay. Welcome, one and all. And as it said on the TV when you were walking in, but just to make sure everybody knows, this is EE261...
热度：51
Brad G. Osgood(斯坦福大学) We are on the air. Okay. Welcome, one and all. And as it said on the TV when you were walking in, but just to make sure everybody knows, this is EE261...
热度：51
85Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding[深度压缩：使用剪枝、训练量化和哈夫曼编码对深层神经网络进行压缩]
Song Han(斯坦福大学) Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware res...
热度：53
Song Han(斯坦福大学) Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware res...
热度：53
86Alternating Direction Method of Multipliers[乘子交替方向法]
Stephen P. Boyd(斯坦福大学) Problems in areas such as machine learning and dynamic optimization on a large network lead to extremely large convex optimization problems, with prob...
热度：53
Stephen P. Boyd(斯坦福大学) Problems in areas such as machine learning and dynamic optimization on a large network lead to extremely large convex optimization problems, with prob...
热度：53
87Social Media Analytics[社交媒体分析]
Jure Leskovec(斯坦福大学) Online social media represent a fundamental shift of how information is being produced, transferred and consumed. The present tutorial investigates te...
热度：43
Jure Leskovec(斯坦福大学) Online social media represent a fundamental shift of how information is being produced, transferred and consumed. The present tutorial investigates te...
热度：43
88Deep Learning on Graphs[图的深度学习]
Jure Leskovec(斯坦福大学) Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social netwo...
热度：46
Jure Leskovec(斯坦福大学) Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social netwo...
热度：46
89Uncertainties in Climate Forecasts: Causes, Magnitudes and Policy Implications[气候预测中的不确定性：原因、程度和政策含义]
Stephen Schneider(斯坦福大学) As one of the lead researchers in the ongoing Intergovernmental Panel on Climate Change (IPCC), which informs the UN Framework Convention on Climate C...
热度：43
Stephen Schneider(斯坦福大学) As one of the lead researchers in the ongoing Intergovernmental Panel on Climate Change (IPCC), which informs the UN Framework Convention on Climate C...
热度：43
90Domain Specific Languages for Convex Optimization[凸优化的领域专用语言]
Stephen P. Boyd(斯坦福大学) Specialized languages for describing convex optimization problems, and associated parsers that automatically transform them to canonical form, have gr...
热度：81
Stephen P. Boyd(斯坦福大学) Specialized languages for describing convex optimization problems, and associated parsers that automatically transform them to canonical form, have gr...
热度：81