機器學習技法 學習筆記 (7):Radial Basis Function Network與Matrix Factorization
Posted on April 22, 2017 in AI.ML. View: 14,508
本篇內容涵蓋Radial Basis Function (RBF) Network、K-Means、One-Hot Encoding和Matrix Factorization
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機器學習技法 學習筆記 (6):神經網路(Neural Network)與深度學習(Deep Learning)
Posted on April 17, 2017 in AI.ML. View: 33,722
本篇內容涵蓋神經網路(Neural Network, NN)、深度學習(Deep Learning, DL)、反向傳播算法(Backpropagation, BP)、Weight-elimination Regularizer、Early Stop、Autoencoder、Principal Component Analysis (PCA)
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機器學習技法 學習筆記 (5):Boost Aggregation Models
Posted on April 02, 2017 in AI.ML. View: 4,892
本篇內容涵蓋AdaBoost (Adaptive Boost)、Gradient Boost、AdaBoosted Decision Tree和Gradient Boosted Decision Tree (GBDT)。
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機器學習技法 學習筆記 (4):Basic Aggregation Models
Posted on March 29, 2017 in AI.ML. View: 7,931
機器學習技法 學習筆記 (3):Kernel Regression
Posted on March 15, 2017 in AI.ML. View: 6,007
本篇內容涵蓋Probabilistic SVM、Kernel Logistic Regression、Kernel Ridge Regression、Support Vector Regression (SVR)
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機器學習技法 學習筆記 (2):Support Vector Machine (SVM)
Posted on February 20, 2017 in AI.ML. View: 11,537
本篇內容涵蓋Hard-Margin Support Vector Machine (SVM)、Kernel Function、Kernel Hard-Margin SVM、Soft-Margin SVM、Kernel Soft-Margin SVM、拉格朗日乘子法(Lagrange Multiplier)、Lagrangian Dual Problem
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機器學習技法 學習筆記 (1):我們將會學到什麼? 先見林再來見樹
Posted on January 12, 2017 in AI.ML. View: 10,667
有什麼特徵可以使用? / Embedding Numerous Features :Kernel Models / Combining Predictive Features:Aggregation Models / Distilling Implicit Features:Extraction Models
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