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种子名称： 林軒田(Hsuan-Tien Lin) - [機器學習基石]Machine Learning Foundations
文件类型： 视频
文件数目： 65个文件
文件大小： 2.04 GB
收录时间： 2019-2-24 21:03
已经下载： 3次
资源热度： 182
最近下载： 2024-12-25 03:35

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林軒田(Hsuan-Tien Lin) - [機器學習基石]Machine Learning Foundations.torrent

P10 3-1-Learning with Different Output Space @ Machine Learning Foundations (機器學習基石).mp464.54MB

P11 3-2-Learning with Different Data Label @ Machine Learning Foundations (機器學習基石).mp458.8MB

P13 3-4-Learning with Different Input Space @ Machine Learning Foundations (機器學習基石).mp446.5MB

P30 8-1-Noise and Probabilistic Target @ Machine Learning Foundations (機器學習基石).mp445.33MB

P2 1-2-What Is Machine Learning @ Machine Learning Foundations (機器學習基石).mp444.01MB

P46 12-1-Quadratic Hypotheses @ Machine Learning Foundations (機器學習基石).mp443.16MB

P3 1-3-Applications of Machine Learning @ Machine Learning Foundations (機器學習基石).mp442.19MB

P54 14-1-Regularized Hypothesis Set @ Machine Learning Foundations (機器學習基石).mp441.09MB

P55 14-2-Weight Decay Regularization @ Machine Learning Foundations (機器學習基石).mp440.79MB

P19 5-2-Effective Number of Lines @ Machine Learning Foundations (機器學習基石).mp440.69MB

P12 3-3-Learning with Different Protocol @ Machine Learning Foundations (機器學習基石).mp439.81MB

P9 2-4-Non-Separable Data @ Machine Learning Foundations (機器學習基石).mp439.56MB

P58 15-1-Model Selection Problem @ Machine Learning Foundations (機器學習基石).mp438.68MB

P42 11-1-Linear Models for Binary Classification @ Machine Learning Foundations (機器學習基石).mp438.55MB

P18 5-1-Recap and Preview @ Machine Learning Foundations (機器學習基石).mp437.97MB

P16 4-3-Connection to Learning @ Machine Learning Foundations (機器學習基石).mp437.83MB

P17 4-4-Connection to Real Learning @ Machine Learning Foundations (機器學習基石).mp437.7MB

P14 4-1-Learning is Impossible @ Machine Learning Foundations (機器學習基石).mp437.62MB

P41 10-4-Gradient Descent @ Machine Learning Foundations (機器學習基石).mp437.56MB

P52 13-3-Deterministic Noise @ Machine Learning Foundations (機器學習基石).mp437.5MB

P26 7-1-Definition of VC Dimension @ Machine Learning Foundations (機器學習基石).mp436.36MB

P48 12-3-Price of Nonlinear Transform @ Machine Learning Foundations (機器學習基石).mp436.34MB

P36 9-3-Generalization Issue @ Machine Learning Foundations (機器學習基石).mp435.92MB

P63 16-2-Sampling Bias @ Machine Learning Foundations (機器學習基石).mp434.56MB

P25 6-4-A Pictorial Proof @ Machine Learning Foundations (機器學習基石).mp433.9MB

P20 5-3-Effective Number of Hypotheses @ Machine Learning Foundations (機器學習基石).mp433.43MB

P1 1-1-Course Introduction @ Machine Learning Foundations (機器學習基石).mp433.27MB

P51 13-2-The Role of Noise and Data Size @ Machine Learning Foundations (機器學習基石).mp432.91MB

P6 2-1-Perceptron Hypothesis Set @ Machine Learning Foundations (機器學習基石).mp432.7MB

P50 13-1-What is Overfitting @ Machine Learning Foundations (機器學習基石).mp432.4MB

P43 11-2-Stochastic Gradient Descent @ Machine Learning Foundations (機器學習基石).mp432.32MB

P64 16-3-Data Snooping @ Machine Learning Foundations (機器學習基石).mp432.16MB

P4 1-4-Components of Learning @ Machine Learning Foundations (機器學習基石).mp432.12MB

P33 8-4-Weighted Classification @ Machine Learning Foundations (機器學習基石).mp431.98MB

P15 4-2-Probability to the Rescue @ Machine Learning Foundations (機器學習基石).mp431.8MB

P57 14-4-General Regularizers @ Machine Learning Foundations (機器學習基石).mp431.28MB

P22 6-1-Restriction of Break Point @ Machine Learning Foundations (機器學習基石).mp430.46MB

P24 6-3-Bounding Function Inductive Cases @ Machine Learning Foundations (機器學習基石).mp430.07MB

P29 7-4-Interpreting VC Dimension @ Machine Learning Foundations (機器學習基石).mp429.41MB

P61 15-4-V-Fold Cross Validation @ Machine Learning Foundations (機器學習基石).mp428.66MB

P40 10-3-Gradient of Logistic Regression Error @ Machine Learning Foundations (機器學習基石).mp428.14MB

P27 7-2-VC Dimension of Perceptrons @ Machine Learning Foundations (機器學習基石).mp427.26MB

P32 8-3-Algorithmic Error Measure @ Machine Learning Foundations (機器學習基石).mp426.91MB

P35 9-2-Linear Regression Algorithm @ Machine Learning Foundations (機器學習基石).mp426.89MB

P62 16-1-Occam's Razor @ Machine Learning Foundations (機器學習基石).mp426.81MB

P39 10-2-Logistic Regression Error @ Machine Learning Foundations (機器學習基石).mp426.44MB

P34 9-1-Linear Regression Problem @ Machine Learning Foundations (機器學習基石).mp426.37MB

P44 11-3-Multiclass via Logistic Regression @ Machine Learning Foundations (機器學習基石).mp426.27MB

P56 14-3-Regularization and VC Theory @ Machine Learning Foundations (機器學習基石).mp425.96MB

P37 9-4-Linear Regression for Binary Classification @ Machine Learning Foundations (機器學習基石).mp425.92MB

P8 2-3-Guarantee of PLA @ Machine Learning Foundations (機器學習基石).mp425.68MB

P7 2-2-Perceptron Learning Algorithm @ Machine Learning Foundations (機器學習基石).mp425.12MB

P21 5-4-Break Point @ Machine Learning Foundations (機器學習基石).mp423.63MB

P60 15-3-Leave-One-Out Cross Validation @ Machine Learning Foundations (機器學習基石).mp423.35MB

P31 8-2-Error Measure @ Machine Learning Foundations (機器學習基石).mp422.93MB

P53 13-4-Dealing with Overfitting @ Machine Learning Foundations (機器學習基石).mp422.71MB

P38 10-1-Logistic Regression Problem @ Machine Learning Foundations (機器學習基石).mp422.69MB

P59 15-2-Validation @ Machine Learning Foundations (機器學習基石).mp421.76MB

P45 11-4-Multiclass via Binary Classification @ Machine Learning Foundations (機器學習基石).mp421.73MB

P65 16-4-Power of Three @ Machine Learning Foundations (機器學習基石).mp421.1MB

P5 1-5-Machine Learning and Other Fields @ Machine Learning Foundations (機器學習基石).mp419.63MB

P47 12-2-Nonlinear Transform @ Machine Learning Foundations (機器學習基石).mp418.32MB

P49 12-4-Structured Hypothesis Sets @ Machine Learning Foundations (機器學習基石).mp416.91MB

P23 6-2-Bounding Function Basic Cases @ Machine Learning Foundations (機器學習基石).mp416.16MB

P28 7-3-Physical Intuition of VC Dimension @ Machine Learning Foundations (機器學習基石).mp414.61MB