2014斯坦福大学机器学习视频网盘资源

2014斯坦福大学机器学习视频网盘资源 百度云下载

路径：IT1区/ –00新课/ 2022 / 34.七月在线机器学习/ 2014斯坦福大学机器学习mkv视频

2014斯坦福大学机器学习视频网盘资源 百度云目录：

1 – 1 – Welcome (7 min).mkv

1 – 2 – What is Machine Learning_ (7 min).mkv

1 – 3 – Supervised Learning (12 min).mkv

1 – 4 – Unsupervised Learning (14 min).mkv

2 – 1 – Model Representation (8 min).mkv

2 – 2 – Cost Function (8 min).mkv

2 – 3 – Cost Function – Intuition I (11 min).mkv

2 – 4 – Cost Function – Intuition II (9 min).mkv

2 – 5 – Gradient Descent (11 min).mkv

2 – 6 – Gradient Descent Intuition (12 min).mkv

2 – 7 – GradientDescentForLinearRegression (6 min).mkv

2 – 8 – What_’s Next (6 min).mkv

3 – 1 – Matrices and Vectors (9 min).mkv

3 – 2 – Addition and Scalar Multiplication (7 min).mkv

3 – 3 – Matrix Vector Multiplication (14 min).mkv

3 – 4 – Matrix Matrix Multiplication (11 min).mkv

3 – 5 – Matrix Multiplication Properties (9 min).mkv

3 – 6 – Inverse and Transpose (11 min).mkv

4 – 1 – Multiple Features (8 min).mkv

4 – 2 – Gradient Descent for Multiple Variables (5 min).mkv

4 – 3 – Gradient Descent in Practice I – Feature Scaling (9 min).mkv

4 – 4 – Gradient Descent in Practice II – Learning Rate (9 min).mkv

4 – 5 – Features and Polynomial Regression (8 min).mkv

4 – 6 – Normal Equation (16 min).mkv

4 – 7 – Normal Equation Noninvertibility (Optional) (6 min).mkv

5 – 1 – Basic Operations (14 min).mkv

5 – 2 – Moving Data Around (16 min).mkv

5 – 3 – Computing on Data (13 min).mkv

5 – 4 – Plotting Data (10 min).mkv

5 – 5 – Control Statements_ for, while, if statements (13 min).mkv

5 – 6 – Vectorization (14 min).mkv

5 – 7 – Working on and Submitting Programming Exercises (4 min).mkv

6 – 1 – Classification (8 min).mkv

6 – 2 – Hypothesis Representation (7 min).mkv

6 – 3 – Decision Boundary (15 min).mkv

6 – 4 – Cost Function (11 min).mkv

6 – 5 – Simplified Cost Function and Gradient Descent (10 min).mkv

6 – 6 – Advanced Optimization (14 min).mkv

6 – 7 – Multiclass Classification_ One-vs-all (6 min).mkv

7 – 1 – The Problem of Overfitting (10 min).mkv

7 – 2 – Cost Function (10 min).mkv

7 – 3 – Regularized Linear Regression (11 min).mkv

7 – 4 – Regularized Logistic Regression (9 min).mkv

8 – 1 – Non-linear Hypotheses (10 min).mkv

8 – 2 – Neurons and the Brain (8 min).mkv

8 – 3 – Model Representation I (12 min).mkv

8 – 4 – Model Representation II (12 min).mkv

8 – 5 – Examples and Intuitions I (7 min).mkv

8 – 6 – Examples and Intuitions II (10 min).mkv

8 – 7 – Multiclass Classification (4 min).mkv

9 – 1 – Cost Function (7 min).mkv

9 – 2 – Backpropagation Algorithm (12 min).mkv

9 – 3 – Backpropagation Intuition (13 min).mkv

9 – 4 – Implementation Note_ Unrolling Parameters (8 min).mkv

9 – 5 – Gradient Checking (12 min).mkv

9 – 6 – Random Initialization (7 min).mkv

9 – 7 – Putting It Together (14 min).mkv

9 – 8 – Autonomous Driving (7 min).mkv

10 – 1 – Deciding What to Try Next (6 min).mkv

10 – 2 – Evaluating a Hypothesis (8 min).mkv

10 – 3 – Model Selection and Train_Validation_Test Sets (12 min).mkv

10 – 4 – Diagnosing Bias vs. Variance (8 min).mkv

10 – 5 – Regularization and Bias_Variance (11 min).mkv

10 – 6 – Learning Curves (12 min).mkv

10 – 7 – Deciding What to Do Next Revisited (7 min).mkv

11 – 1 – Prioritizing What to Work On (10 min).mkv

11 – 2 – Error Analysis (13 min).mkv

11 – 3 – Error Metrics for Skewed Classes (12 min).mkv

11 – 4 – Trading Off Precision and Recall (14 min).mkv

11 – 5 – Data For Machine Learning (11 min).mkv

12 – 1 – Optimization Objective (15 min).mkv

12 – 2 – Large Margin Intuition (11 min).mkv

12 – 3 – Mathematics Behind Large Margin Classification (Optional) (20 min).mkv

12 – 4 – Kernels I (16 min).mkv

12 – 5 – Kernels II (16 min).mkv

12 – 6 – Using An SVM (21 min).mkv

13 – 1 – Unsupervised Learning_ Introduction (3 min).mkv

13 – 2 – K-Means Algorithm (13 min).mkv

13 – 3 – Optimization Objective (7 min)(1).mkv

13 – 3 – Optimization Objective (7 min).mkv

13 – 4 – Random Initialization (8 min).mkv

13 – 5 – Choosing the Number of Clusters (8 min).mkv

14 – 1 – Motivation I_ Data Compression (10 min).mkv

14 – 2 – Motivation II_ Visualization (6 min).mkv

14 – 3 – Principal Component Analysis Problem Formulation (9 min).mkv

14 – 4 – Principal Component Analysis Algorithm (15 min).mkv

14 – 5 – Choosing the Number of Principal Components (11 min).mkv

14 – 6 – Reconstruction from Compressed Representation (4 min).mkv

14 – 7 – Advice for Applying PCA (13 min).mkv

15 – 1 – Problem Motivation (8 min).mkv

15 – 2 – Gaussian Distribution (10 min).mkv

15 – 3 – Algorithm (12 min).mkv

15 – 4 – Developing and Evaluating an Anomaly Detection System (13 min).mkv

15 – 5 – Anomaly Detection vs. Supervised Learning (8 min).mkv

15 – 6 – Choosing What Features to Use (12 min).mkv

15 – 7 – Multivariate Gaussian Distribution (Optional) (14 min).mkv

15 – 8 – Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv

16 – 1 – Problem Formulation (8 min).mkv

16 – 2 – Content Based Recommendations (15 min).mkv

16 – 3 – Collaborative Filtering (10 min).mkv

16 – 4 – Collaborative Filtering Algorithm (9 min).mkv

16 – 5 – Vectorization_ Low Rank Matrix Factorization (8 min).mkv

16 – 6 – Implementational Detail_ Mean Normalization (9 min).mkv

17 – 1 – Learning With Large Datasets (6 min).mkv

17 – 2 – Stochastic Gradient Descent (13 min).mkv

17 – 3 – Mini-Batch Gradient Descent (6 min).mkv

17 – 4 – Stochastic Gradient Descent Convergence (12 min).mkv

17 – 5 – Online Learning (13 min).mkv

17 – 6 – Map Reduce and Data Parallelism (14 min).mkv

18 – 1 – Problem Description and Pipeline (7 min).mkv

18 – 2 – Sliding Windows (15 min).mkv

18 – 3 – Getting Lots of Data and Artificial Data (16 min).mkv

18 – 4 – Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv

19 – 1 – Summary and Thank You (5 min).mkv

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