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种子名称： [FreeCoursesOnline.Me] Coursera - Machine Learning
文件类型： 视频
文件数目： 113个文件
文件大小： 1.81 GB
收录时间： 2019-7-19 22:03
已经下载： 3次
资源热度： 133
最近下载： 2024-12-27 01:38

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[FreeCoursesOnline.Me] Coursera - Machine Learning.torrent

001.Welcome/001. Welcome to Machine Learning!.mp49.13MB

002.Introduction/002. Welcome.mp418.28MB

002.Introduction/003. What is Machine Learning.mp411.41MB

002.Introduction/004. Supervised Learning.mp416.68MB

002.Introduction/005. Unsupervised Learning.mp423.33MB

003.Model and Cost Function/006. Model Representation.mp411.42MB

003.Model and Cost Function/007. Cost Function.mp411.51MB

003.Model and Cost Function/008. Cost Function - Intuition I.mp415.53MB

003.Model and Cost Function/009. Cost Function - Intuition II.mp416.99MB

004.Parameter Learning/010. Gradient Descent.mp418.72MB

004.Parameter Learning/011. Gradient Descent Intuition.mp416.61MB

004.Parameter Learning/012. Gradient Descent For Linear Regression.mp416.43MB

005.Linear Algebra Review/013. Matrices and Vectors.mp411.94MB

005.Linear Algebra Review/014. Addition and Scalar Multiplication.mp49.27MB

005.Linear Algebra Review/015. Matrix Vector Multiplication.mp418.93MB

005.Linear Algebra Review/016. Matrix Matrix Multiplication.mp416.29MB

005.Linear Algebra Review/017. Matrix Multiplication Properties.mp412.15MB

005.Linear Algebra Review/018. Inverse and Transpose.mp417.01MB

006.Multivariate Linear Regression/019. Multiple Features.mp411.58MB

006.Multivariate Linear Regression/020. Gradient Descent for Multiple Variables.mp47.62MB

006.Multivariate Linear Regression/021. Gradient Descent in Practice I - Feature Scaling.mp412.94MB

006.Multivariate Linear Regression/022. Gradient Descent in Practice II - Learning Rate.mp412.56MB

006.Multivariate Linear Regression/023. Features and Polynomial Regression.mp411.54MB

007.Computing Parameters Analytically/024. Normal Equation.mp423.63MB

007.Computing Parameters Analytically/025. Normal Equation Noninvertibility.mp48.8MB

008.Submitting Programming Assignments/026. Working on and Submitting Programming Assignments.mp48.96MB

009.Octave Matlab Tutorial/027. Basic Operations.mp424.9MB

009.Octave Matlab Tutorial/028. Moving Data Around.mp429.53MB

009.Octave Matlab Tutorial/029. Computing on Data.mp419.81MB

009.Octave Matlab Tutorial/030. Plotting Data.mp420.08MB

009.Octave Matlab Tutorial/031. Control Statements for, while, if statement.mp423.88MB

009.Octave Matlab Tutorial/032. Vectorization.mp422.27MB

010.Classification and Representation/033. Classification.mp411.32MB

010.Classification and Representation/034. Hypothesis Representation.mp411.17MB

010.Classification and Representation/035. Decision Boundary.mp422.19MB

011.Logistic Regression Model/036. Cost Function.mp415.83MB

011.Logistic Regression Model/037. Simplified Cost Function and Gradient Descent.mp416.26MB

011.Logistic Regression Model/038. Advanced Optimization.mp426.77MB

012.Multiclass Classification/039. Multiclass Classification One-vs-all.mp49.07MB

013.Solving the Problem of Overfitting/040. The Problem of Overfitting.mp414.93MB

013.Solving the Problem of Overfitting/041. Cost Function.mp415.51MB

013.Solving the Problem of Overfitting/042. Regularized Linear Regression.mp415.63MB

013.Solving the Problem of Overfitting/043. Regularized Logistic Regression.mp416.77MB

014.Motivations/044. Non-linear Hypotheses.mp414.74MB

014.Motivations/045. Neurons and the Brain.mp414.57MB

015.Neural Networks/046. Model Representation I.mp418MB

015.Neural Networks/047. Model Representation II.mp418.4MB

016.Applications/048. Examples and Intuitions I.mp410.07MB

016.Applications/049. Examples and Intuitions II.mp420.93MB

016.Applications/050. Multiclass Classification.mp47MB

017.Cost Function and Backpropagation/051. Cost Function.mp410.25MB

017.Cost Function and Backpropagation/052. Backpropagation Algorithm.mp419.07MB

017.Cost Function and Backpropagation/053. Backpropagation Intuition.mp422.23MB

018.Backpropagation in Practice/054. Implementation Note Unrolling Parameters.mp412.92MB

018.Backpropagation in Practice/055. Gradient Checking.mp418.35MB

018.Backpropagation in Practice/056. Random Initialization.mp49.81MB

018.Backpropagation in Practice/057. Putting It Together.mp423.55MB

019.Application of Neural Networks/058. Autonomous Driving.mp428.3MB

020.Evaluating a Learning Algorithm/059. Deciding What to Try Next.mp49.35MB

020.Evaluating a Learning Algorithm/060. Evaluating a Hypothesis.mp411.05MB

020.Evaluating a Learning Algorithm/061. Model Selection and Train Validation Test Sets.mp419.04MB

021.Bias vs. Variance/062. Diagnosing Bias vs. Variance.mp412.18MB

021.Bias vs. Variance/063. Regularization and Bias Variance.mp416.39MB

021.Bias vs. Variance/064. Learning Curves.mp416.39MB

021.Bias vs. Variance/065. Deciding What to Do Next Revisited.mp411.43MB

022.Building a Spam Classifier/066. Prioritizing What to Work On.mp415.06MB

022.Building a Spam Classifier/067. Error Analysis.mp421.27MB

023.Handling Skewed Data/068. Error Metrics for Skewed Classes.mp417.95MB

023.Handling Skewed Data/069. Trading Off Precision and Recall.mp421.3MB

024.Using Large Data Sets/070. Data For Machine Learning.mp417.31MB

025.Large Margin Classification/071. Optimization Objective.mp421.89MB

025.Large Margin Classification/072. Large Margin Intuition.mp415.21MB

025.Large Margin Classification/073. Mathematics Behind Large Margin Classification.mp428.48MB

026.Kernels/074. Kernels I.mp422.81MB

026.Kernels/075. Kernels II.mp422.63MB

027.SVMs in Practice/076. Using An SVM.mp431.99MB

028.Clustering/077. Unsupervised Learning Introduction.mp45.16MB

028.Clustering/078. K-Means Algorithm.mp417.67MB

028.Clustering/079. Optimization Objective.mp410.92MB

028.Clustering/080. Random Initialization.mp411.15MB

028.Clustering/081. Choosing the Number of Clusters.mp412.22MB

029.Motivation/082. Motivation I Data Compression.mp421.45MB

029.Motivation/083. Motivation II Visualization.mp48.3MB

030.Principal Component Analysis/084. Principal Component Analysis Problem Formulation.mp413.98MB

030.Principal Component Analysis/085. Principal Component Analysis Algorithm.mp424.29MB

031.Applying PCA/086. Reconstruction from Compressed Representation.mp47.16MB

031.Applying PCA/087. Choosing the Number of Principal Components.mp415.64MB

031.Applying PCA/088. Advice for Applying PCA.mp419.74MB

032.Density Estimation/089. Problem Motivation.mp410.56MB

032.Density Estimation/090. Gaussian Distribution.mp415.19MB

032.Density Estimation/091. Algorithm.mp418.94MB

033.Building an Anomaly Detection System/092. Developing and Evaluating an Anomaly Detection System.mp420.53MB

033.Building an Anomaly Detection System/093. Anomaly Detection vs. Supervised Learning.mp413.15MB

033.Building an Anomaly Detection System/094. Choosing What Features to Use.mp419.09MB

034.Multivariate Gaussian Distribution (Optional)/095. Multivariate Gaussian Distribution.mp421.86MB

034.Multivariate Gaussian Distribution (Optional)/096. Anomaly Detection using the Multivariate Gaussian Distribution.mp422.42MB

035.Predicting Movie Ratings/097. Problem Formulation.mp416.41MB

035.Predicting Movie Ratings/098. Content Based Recommendations.mp423.19MB

036.Collaborative Filtering/099. Collaborative Filtering.mp415.52MB

036.Collaborative Filtering/100. Collaborative Filtering Algorithm.mp414.71MB

037.Low Rank Matrix Factorization/101. Vectorization Low Rank Matrix Factorization.mp412.82MB

037.Low Rank Matrix Factorization/102. Implementational Detail Mean Normalization.mp412.91MB

038.Gradient Descent with Large Datasets/103. Learning With Large Datasets.mp48.54MB

038.Gradient Descent with Large Datasets/104. Stochastic Gradient Descent.mp420.99MB

038.Gradient Descent with Large Datasets/105. Mini-Batch Gradient Descent.mp49.75MB

038.Gradient Descent with Large Datasets/106. Stochastic Gradient Descent Convergence.mp418.11MB

039.Advanced Topics/107. Online Learning.mp420.51MB

039.Advanced Topics/108. Map Reduce and Data Parallelism.mp421.23MB

040.Photo OCR/109. Problem Description and Pipeline.mp410.42MB

040.Photo OCR/110. Sliding Windows.mp421.93MB

040.Photo OCR/111. Getting Lots of Data and Artificial Data.mp425.3MB

040.Photo OCR/112. Ceiling Analysis What Part of the Pipeline to Work on Next.mp421.92MB

041.Conclusion/113. Summary and Thank You.mp49.08MB