Andrew Ng

Results: 129



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31Depth Estimation using Monocular and Stereo Cues Ashutosh Saxena, Jamie Schulte and Andrew Y. Ng Computer Science Department Stanford University, Stanford, CA 94305 {asaxena,schulte,ang}@cs.stanford.edu

Depth Estimation using Monocular and Stereo Cues Ashutosh Saxena, Jamie Schulte and Andrew Y. Ng Computer Science Department Stanford University, Stanford, CA 94305 {asaxena,schulte,ang}@cs.stanford.edu

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Source URL: www.cs.cornell.edu

Language: English - Date: 2009-07-21 20:22:23
    32CS229 Lecture notes Andrew Ng 1 The perceptron and large margin classifiers

    CS229 Lecture notes Andrew Ng 1 The perceptron and large margin classifiers

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    Source URL: cs229.stanford.edu

    Language: English - Date: 2012-11-26 03:25:38
      33CS229 Lecture notes Andrew Ng Mixtures of Gaussians and the EM algorithm In this set of notes, we discuss the EM (Expectation-Maximization) for density estimation. Suppose that we are given a training set {x(1) , . . . ,

      CS229 Lecture notes Andrew Ng Mixtures of Gaussians and the EM algorithm In this set of notes, we discuss the EM (Expectation-Maximization) for density estimation. Suppose that we are given a training set {x(1) , . . . ,

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      Source URL: cs229.stanford.edu

      Language: English - Date: 2012-11-26 03:26:12
        34CS229 Lecture notes Andrew Ng Part XIII Reinforcement Learning and

        CS229 Lecture notes Andrew Ng Part XIII Reinforcement Learning and

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        Source URL: cs229.stanford.edu

        Language: English - Date: 2012-11-26 03:35:03
          35Optical Illusion Sara Bolouki, Roger Grosse, Honglak Lee, Andrew Ng 1. Introduction The goal of this project is to explain some of the illusory phenomena using sparse coding and whitening model. Instead of the sparse cod

          Optical Illusion Sara Bolouki, Roger Grosse, Honglak Lee, Andrew Ng 1. Introduction The goal of this project is to explain some of the illusory phenomena using sparse coding and whitening model. Instead of the sparse cod

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          Source URL: cs229.stanford.edu

          Language: English - Date: 2011-09-14 20:33:06
            36CS229 Lecture notes Andrew Ng Part VII Regularization and model

            CS229 Lecture notes Andrew Ng Part VII Regularization and model

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            Source URL: cs229.stanford.edu

            Language: English - Date: 2012-11-26 03:25:12
              37CS229 Lecture notes Andrew Ng Part XII Independent Components

              CS229 Lecture notes Andrew Ng Part XII Independent Components

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              Source URL: cs229.stanford.edu

              Language: English - Date: 2012-11-26 03:34:43
                38Feature selection, L1 vs. L2 regularization, and rotational invariance Andrew Ng ICML 2004 Presented by Paul Hammon

                Feature selection, L1 vs. L2 regularization, and rotational invariance Andrew Ng ICML 2004 Presented by Paul Hammon

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                Source URL: cseweb.ucsd.edu

                Language: English - Date: 2007-03-06 18:56:02
                  393-D Reconstruction from Sparse Views using Monocular Vision Ashutosh Saxena, Min Sun and Andrew Y. Ng Computer Science Department, Stanford University, Stanford, CA 94305 {asaxena,aliensun,ang}@cs.stanford.edu Abstract

                  3-D Reconstruction from Sparse Views using Monocular Vision Ashutosh Saxena, Min Sun and Andrew Y. Ng Computer Science Department, Stanford University, Stanford, CA 94305 {asaxena,aliensun,ang}@cs.stanford.edu Abstract

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                  Source URL: ai.stanford.edu

                  Language: English - Date: 2007-09-02 15:50:38
                    40High Speed Obstacle Avoidance using Monocular Vision and Reinforcement Learning Jeff Michels Ashutosh Saxena Andrew Y. Ng

                    High Speed Obstacle Avoidance using Monocular Vision and Reinforcement Learning Jeff Michels Ashutosh Saxena Andrew Y. Ng

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                    Source URL: ai.stanford.edu

                    Language: English - Date: 2005-06-01 18:39:04