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种子名称： 2014斯坦福大学机器学习mkv视频
文件类型： 视频
文件数目： 129个文件
文件大小： 1.33 GB
收录时间： 2017-6-13 14:07
已经下载： 3次
资源热度： 137
最近下载： 2024-9-10 12:28

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2014斯坦福大学机器学习mkv视频.torrent

1 - 1 - Welcome (7 min).mkv11.69MB

1 - 2 - What is Machine Learning_ (7 min).mkv9.25MB

1 - 3 - Supervised Learning (12 min).mkv13.25MB

1 - 4 - Unsupervised Learning (14 min).mkv16.45MB

10 - 1 - Deciding What to Try Next (6 min).mkv6.78MB

10 - 2 - Evaluating a Hypothesis (8 min).mkv8.36MB

10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv14.92MB

10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv8.86MB

10 - 5 - Regularization and Bias_Variance (11 min).mkv12.42MB

10 - 6 - Learning Curves (12 min).mkv12.74MB

10 - 7 - Deciding What to Do Next Revisited (7 min).mkv8.08MB

11 - 1 - Prioritizing What to Work On (10 min).mkv11.03MB

11 - 2 - Error Analysis (13 min).mkv15.22MB

11 - 3 - Error Metrics for Skewed Classes (12 min).mkv13.07MB

11 - 4 - Trading Off Precision and Recall (14 min).mkv15.77MB

11 - 5 - Data For Machine Learning (11 min).mkv12.7MB

12 - 1 - Optimization Objective (15 min).mkv16.42MB

12 - 2 - Large Margin Intuition (11 min).mkv11.65MB

12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv21.51MB

12 - 4 - Kernels I (16 min).mkv17.32MB

12 - 5 - Kernels II (16 min).mkv17.2MB

12 - 6 - Using An SVM (21 min).mkv23.63MB

13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv3.76MB

13 - 2 - K-Means Algorithm (13 min).mkv13.61MB

13 - 3 - Optimization Objective (7 min)(1).mkv8.04MB

13 - 3 - Optimization Objective (7 min).mkv8.03MB

13 - 4 - Random Initialization (8 min).mkv8.56MB

13 - 5 - Choosing the Number of Clusters (8 min).mkv9.28MB

14 - 1 - Motivation I_ Data Compression (10 min).mkv14.15MB

14 - 2 - Motivation II_ Visualization (6 min).mkv6.22MB

14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv10.32MB

14 - 4 - Principal Component Analysis Algorithm (15 min).mkv17.55MB

14 - 5 - Choosing the Number of Principal Components (11 min).mkv11.67MB

14 - 6 - Reconstruction from Compressed Representation (4 min).mkv4.92MB

14 - 7 - Advice for Applying PCA (13 min).mkv14.5MB

15 - 1 - Problem Motivation (8 min).mkv8.23MB

15 - 2 - Gaussian Distribution (10 min).mkv11.53MB

15 - 3 - Algorithm (12 min).mkv13.77MB

15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv14.96MB

15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv9.17MB

15 - 6 - Choosing What Features to Use (12 min).mkv13.93MB

15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv15.72MB

15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv16.12MB

16 - 1 - Problem Formulation (8 min).mkv10.57MB

16 - 2 - Content Based Recommendations (15 min).mkv16.71MB

16 - 3 - Collaborative Filtering (10 min).mkv11.6MB

16 - 4 - Collaborative Filtering Algorithm (9 min).mkv10.18MB

16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv9.55MB

16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv9.58MB

17 - 1 - Learning With Large Datasets (6 min).mkv6.41MB

17 - 2 - Stochastic Gradient Descent (13 min).mkv15.12MB

17 - 3 - Mini-Batch Gradient Descent (6 min).mkv7.22MB

17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv13.15MB

17 - 5 - Online Learning (13 min).mkv14.72MB

17 - 6 - Map Reduce and Data Parallelism (14 min).mkv15.84MB

18 - 1 - Problem Description and Pipeline (7 min).mkv7.81MB

18 - 2 - Sliding Windows (15 min).mkv16.3MB

18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv18.57MB

18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv15.9MB

19 - 1 - Summary and Thank You (5 min).mkv6.02MB

2 - 1 - Model Representation (8 min).mkv8.86MB

2 - 2 - Cost Function (8 min).mkv8.91MB

2 - 3 - Cost Function - Intuition I (11 min).mkv12.06MB

2 - 4 - Cost Function - Intuition II (9 min).mkv11.22MB

2 - 5 - Gradient Descent (11 min).mkv13.32MB

2 - 6 - Gradient Descent Intuition (12 min).mkv12.84MB

2 - 7 - GradientDescentForLinearRegression (6 min).mkv12.02MB

2 - 8 - What_'s Next (6 min).mkv5.99MB

3 - 1 - Matrices and Vectors (9 min).mkv9.42MB

3 - 2 - Addition and Scalar Multiplication (7 min).mkv7.35MB

3 - 3 - Matrix Vector Multiplication (14 min).mkv14.78MB

3 - 4 - Matrix Matrix Multiplication (11 min).mkv12.42MB

3 - 5 - Matrix Multiplication Properties (9 min).mkv9.67MB

3 - 6 - Inverse and Transpose (11 min).mkv12.69MB

4 - 1 - Multiple Features (8 min).mkv8.71MB

4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv5.71MB

4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv9.32MB

4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv9.13MB

4 - 5 - Features and Polynomial Regression (8 min).mkv8.15MB

4 - 6 - Normal Equation (16 min).mkv16.88MB

4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv6.15MB

5 - 1 - Basic Operations (14 min).mkv17.5MB

5 - 2 - Moving Data Around (16 min).mkv20.52MB

5 - 3 - Computing on Data (13 min).mkv15.04MB

5 - 4 - Plotting Data (10 min).mkv13.17MB

5 - 5 - Control Statements_ for, while, if statements (13 min).mkv16.29MB

5 - 6 - Vectorization (14 min).mkv15.88MB

5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv5.41MB

6 - 1 - Classification (8 min).mkv8.65MB

6 - 2 - Hypothesis Representation (7 min).mkv8.23MB

6 - 3 - Decision Boundary (15 min).mkv16.51MB

6 - 4 - Cost Function (11 min).mkv12.92MB

6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv11.8MB

6 - 6 - Advanced Optimization (14 min).mkv17.95MB

6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv6.83MB

7 - 1 - The Problem of Overfitting (10 min).mkv11MB

7 - 2 - Cost Function (10 min).mkv11.48MB

7 - 3 - Regularized Linear Regression (11 min).mkv11.84MB

7 - 4 - Regularized Logistic Regression (9 min).mkv10.77MB

8 - 1 - Non-linear Hypotheses (10 min).mkv10.73MB

8 - 2 - Neurons and the Brain (8 min).mkv9.77MB

8 - 3 - Model Representation I (12 min).mkv13.32MB

8 - 4 - Model Representation II (12 min).mkv13.27MB

8 - 5 - Examples and Intuitions I (7 min).mkv7.78MB

8 - 6 - Examples and Intuitions II (10 min).mkv13.84MB

8 - 7 - Multiclass Classification (4 min).mkv4.77MB

9 - 1 - Cost Function (7 min).mkv7.56MB

9 - 2 - Backpropagation Algorithm (12 min).mkv13.75MB

9 - 3 - Backpropagation Intuition (13 min).mkv15.25MB

9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv9.27MB

9 - 5 - Gradient Checking (12 min).mkv13.32MB

9 - 6 - Random Initialization (7 min).mkv7.46MB

9 - 7 - Putting It Together (14 min).mkv16.1MB

9 - 8 - Autonomous Driving (7 min).mkv14.79MB

教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/noisy.dat2.06KB

教程和笔记/Stanford-Machine-Learning-Course-master/HMM/robot_no_momentum.data469.53KB

教程和笔记/Stanford-Machine-Learning-Course-master/HMM/robot_small.data159B

教程和笔记/Stanford-Machine-Learning-Course-master/HMM/robot_with_momentum.data469.53KB

教程和笔记/Stanford-Machine-Learning-Course-master/HMM/weather_all.data397.22KB

教程和笔记/Stanford-Machine-Learning-Course-master/HMM/weather_bos_la.data197.79KB

教程和笔记/Stanford-Machine-Learning-Course-master/HMM/weather_bos_sea.data208.69KB

教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/plotDataPoints.m434B

教程和笔记/Stanford-Machine-Learning-Course-master/Neural network learning/mlclass-ex4/checkNNGradients.m1.9KB

教程和笔记/Stanford-Machine-Learning-Course-master/Neural network learning/mlclass-ex4/debugInitializeWeights.m841B

教程和笔记/Stanford-Machine-Learning-Course-master/Neural network learning/mlclass-ex4/randInitializeWeights.m982B

机器学习课程2014源代码/mlclass-ex4-jin/checkNNGradients.m1.9KB

机器学习课程2014源代码/mlclass-ex4-jin/debugInitializeWeights.m841B

机器学习课程2014源代码/mlclass-ex4-jin/randInitializeWeights.m980B

机器学习课程2014源代码/mlclass-ex7-jin/plotDataPoints.m434B