2014
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› The Formula of FTRL-proximal -
› 基本的数据分析工作 -
› Basic Concepts in Information Theory -
› Model Assessment and Selection -
› Linear Methods for Classification -
› Linear Methods for Regression -
› Greedy Function Approxiation: A Gradient Boosting Machine -
› Notes for Conjugate Gradient Menthod -
› Support Vector Machines -
› Machine learning in 10 pictures -
› Scala & Spark Links -
› 2014 翔安 -
› Serendipitous Entity Search on User-generated Content -
› Chapter 8 Geometric Problems Notes -
› Chapter 7. Statistical Estimation Notes -
› Numpy Tips -
› Chapter 6. Norm Approximation Notes -
› Chapter 5. Duality Notes -
› Chapter 4. Convex Optimization Problems Notes -
› Chapter 4. Convex Optimization Problems Notes -
› Algorithms in Probabilistic Graphical Models -
› Probabilistic Graphical Models Learning Records -
› Stochastic Gradient descent (SGD) vs Alternating least squares (ALS) for Matrix Factorization -
› Item-based Collaborative Filtering -
› Evaluation of Recommemder Systems -
› User-based Collaborative Filtering -
› Non-negative Matrix Factorization -
› Content-based Recommendations -
› Chapter 1. Convex Optimization ——Introduction -
› GraphLab Introduction -
› GraphLab Learning Records -
› MPI Learning Records