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教程和笔记
Deeplearning深度学习笔记v5.61.pdf
机器学习个人笔记完整版v4.21.pdf
教材
deeplearningbook.pdf
机器学习周志华.pdf
统计学习方法-李航.pdf
PatternRecognitionAndMachineLearning.pdf
dbook_cn_v0.5-beta.pdf
pdf视频
11-1-Welcome (7 min). mkv
-1-Welcome (7 min). srt
1-2-What is Machine Learning_(7 min). mkv
11-2-What is Machine Learning_(7 min). srt
11-3-Supervised Learning (12 min). mkv
1-3-Supervised Learning (12 min). srt
-4-Unsupervised Learning (14 min). mkv
1-4-Unsupervised Learning (14 min). srt
10 -1-Deciding What to Try Next (6 min). mkv
10-1-Deciding What to Try Next (6 min). srt
1 10 -2-Evaluating a Hypothesis (8 min). mkv
10-2-Evaluating a Hypothesis (8 min). srt
1 10-3-Model Selection and Train_Validation_Test Sets (12 min). mky
10-3-Model Selection and Train_validation_Test Sets (12 min). srt
1 10-4-Diagnosing Bias vs. Variance (8 min). mkv
10-4-Diagnosing Bias vs. Variance (8 min). srt
110-5-Regularization and Bias_Variance (11 min). mkv
1 10-5-Regularization and Bias_Variance (11 min). srt
10-6-Learning Curves (12 min). mkv
10-6-Learning Curves (12 min). srt
10-7-Deciding What to Do Next Revisited (7 min). mkv
10-7-Deciding What to Do Next Revisited (7 min). srt
111-1-Prioritizing What to Work On (10 min). mkv
Prioritizing What to Work On (10 min). st
1 11-2-Error Analysis (13 min). mkv
11 -2 -Error Analysis (13 min). srt
111-3-Error Metrics for Skewed Classes (12 min). mkv
111-3-Error Metrics for Skewed Classes (12 min). srt
111-4-Trading Off Precision and Recall (14 min). mkv
1 11-4-Trading Off Precision and Recall (14 min). srt
11-5-Data For Machine Learning (11 min). mkv
1 11-5-Data For Machine Learning (11 min). srt
1 12-1-Optimization Objective (15 min). mkv
1 12-1-Optimization Objective (15 min). srt
1 12-2-Large Margin Intuition (11 min). mku
12-2-Large Margin Intuition (11 min). srt
1 12-3-Mathematics Behind Large Margin Classification (Optional) (20 min). mkv
12-3-Mathematics Behind Large Margin Classification (Optional) (20 min). srt
12-4-Kemels I (16 min). mkv
12-4-Kemels I (16 min). srt
12-5-Kemels II (16 min). mkv
12 -5-Kermels II (16 min). st
12-6-Using An SVM (21 min). mk
12-6-Using An SVM (21 min). srt
13-1-Unsupervised Learning_Introduction (3 min). mkv
13-1-Unsupervised Learning_Introduction (3 min). srt
13-2-K-Means Algorithm (13 min). mky
13-2-K-Means Algorithm (13 min). srt
13-3-Optimization Objective (7 min)(1). mkv
13-3-Optimization Objective (7 min)(1). srt
13-3-Optimization Objective (7 min). mkv
13-3-Optimization Objective (7 min). srt
13-4-Random Initialization (8 min). mky
13-4-Random Initialization (8 min). srt
13-5-Choosing the Number of Clusters (8 min). mkv
13-5-Choosing the Number of Clusters (8 min). srt
14-1-Motivation L_Data Compression (10 min). mkv
14-1-Motivation I_Data Compression (10 min). srt
14-2-Motivation IL Visualization (6 min). mkv
14-2-Motivation I Visualization (6 min). srt
14-3-Principal Component Analysis Problem Formulation (9 min). mkv
14-3-Principal Component Analysis Problem Formulation (9 min). srt
14-4-Principal Component Analysis Algorithm (15 min). mkv
14-4-Principal Component Analysis Algorithm (15 min). srt
14-5-Choosing the Number of Principal Components (11 min). mkv
14-5-Choosing the Number of Principal Components (11 min). srt
14-6-Reconstruction from Compressed Representation (4 min). mkv
14-6-Reconstruction from Compressed Representation (4 min). srt
14-7-Advice for Applying PCA (13 min). mkv
14-7-Advice for Applying PCA (13 min). srt
15-1-Problem Motivation (8 min). mkv
15-1-Problem Motivation (8 min). srt
15-2-Gaussian Distribution (10 min). mkv
15-2-Gaussian Distribution (10 min). srt
115-3-Algorithm (12 min). mkv
15-3-Algorithm (12 min). srt.
15-4-Developing and Evaluating an Anomaly Detection System (13 min). mkv
15-4-Developing and Evaluating an Anomaly Detection System (13 min). srt
1 15-5-Anomaly Detection vs. Supervised Learning (8 min). mkv
15-5-Anomaly Detection vs. Supervised Learning (8 min). srt
115-6-Choosing What Features to Use (12 min). mkv
15-6-Choosing What Features to Use (12 min). srt
1 15-7-Multivariate Gaussian Distribution (Optional) (14 min). mk
15-7-Multivariate Gaussian Distribution (Optional) (14 min). st
15-8-Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min). mkv
15-8-Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min). srt
16-1-Problem Formulation (8 min). mkv
16-1-Problem Formulation (8 min). srt
16-2-Content Based Recommendations (15 min). mku
16-2-Content Based Recommendations (15 min). srt
16 -3-Collaborative Filtering (10 min). mkv
16-3-Collaborative Filtering (10 min). srt
16-4-Collaborative Filtering Algorithm (9 min). mkv
16-4-Collaborative Filtering Algorithm (9 min). srt
16-5-Vectorization_Low Rank Matrix Factorization (8 min). mkv
16-5-Vectorization_Low Rank Matrix Factorization (8 min). srt
16-6-Implementational Detail_Mean Normalization (9 min). mk
16-6-Implementational Detail_Mean Normalization (9 min). srt
17-1-Learning With Large Datasets (6 min). mk
17-1-Learning With Large Datasets (6 min). srt
17-2-Stochastic Gradient Descent (13 min). mkv
17-2-Stochastic Gradient Descent (13 min). srt
17-3-Mini-Batch Gradient Descent (6 min). mku
17-3-Mini-Batch Gradient Descent (6 min). srt
17-4-Stochastic Gradient Descent Convergence (12 min). mkv
17-4-Stochastic Gradient Descent Convergence (12 min). srt
1 17-5-Online Learning (13 min). mkv
17-5-Online Learning (13 min). srt
17-6-Map Reduce and Data Parallelism (14 min). mkv
17-6-Map Reduce and Data Parallelism (14 min). srt
18-1-Problem Description and Pipeline (7 min). mkv
18-1-Problem Description and Pipeline (7 min). srt
18-2-Sliding Windows (15 min). mkv
18-2-Sliding Windows (15 min). srt
18-3-Getting Lots of Data and Artificial Data (16 min). mkv
18-3-Getting Lots of Data and Artificial Data (16 min). srt
18-4-Ciling Analysis_What Part of the Pipeline to Work on Next (14 min). mkv
18-4-Ceiling Analysis_What Part of the Pipeline to Work on Next (14 min). srt
19-1-Summary and Thank You (5 min). mky
19-1-Summary and Thank You (5 min). srt
2-1-Model Representation (8 min). mkv
2-1-Model Representation (8 min). srt
2-2-Cost Function (8 min). mkv
2-2-Cost Function (8 min). srt
2-3-Cost Function -Intuition I (11 min). mkv
2-3-Cost Function -Intuition I (11 min). srt
2-4-Cost Function -Intuition II (9 min). mkv
2-4-Cost Function -Intuition Il (9 min). srt
2-5-Gradient Descent (11 min). mkv
2-5-Gradient Descent (11 min). srt
2-6-Gradient Descent Intuition (12 min). mkv
2-6-Gradient Descent Intuition (12 min). srt
2-7-Gradient Descent For Linear Regression (10 min). srt
2-7-GradientDescentForLinearRegression (6 min). mkv
2-8-What 's Next (6 min). mkv
2-8-What_'s Next (6 min). srt
3-1-Matrices and Vectors (9 min). mky
3-1-Matrices and Vectors (9 min). srt
3-2-Addition and Scalar Multiplication (7 min). mkv
3-2-Addition and Scalar Multiplication (7 min). srt
-3-Matrix Vector Multiplication (14 min). mkv
3-3-Matrix Vector Multiplication (14 min). srt
3-4-Matrix Matrix Multiplication (11 min). mkv
3-4-Matrix Matrix Multiplication (11 min). srt
3-5-Matrix Multiplication Properties (9 min). mkv
3-5-Matrix Multiplication Properties (9 min). srt
3-6-Inverse and Transpose (11 min). mkv
3-6-Inverse and Transpose (11 min). srt
4-1-Multiple Features (8 min). mkv
4-1-Multiple Features (8 min). srt
4-2-Gradient Descent for Multiple Variables (5 min). mkv
4-2-Gradient Descent for Multiple Variables (5 min). srt
4-3-Gradient Descent in Practice I-Feature Scaling (9 min). mkv
4-3-Gradient Descent in Practice I-Feature Scaling (9 min). srt
4-4-Gradient Descent in Practice II -Learning Rate (9 min). mkv
4-4-Gradient Descent in Practice II -Learning Rate (9 min). srt
4-5-Features and Polynomial Regression (8 min). mkv
4-5-Features and Polynomial Regression (8 min). srt
4-6-Normal Equation (16 min). mky
4-6-Normal Equation (16 min). srt
4-7-Normal Equation Noninvertibility (Optional) (6 min). mkv
4-7-Normal Equation Noninvertibility (Optional) (6 min). srt
5-1-Basic Operations (14 min). mkv
5-1-Basic Operations (14 min). srt
5-2-Moving Data Around (16 min). mkv
5-2 -Moving Data Around (16 min). srt
5-3-Computing on Data (13 min). mkv
5-3-Computing on Data (13 min). srt
5-4-Plotting Data (10 min). mkv
5-4-Plotting Data (10 min). srt
5-5-Control Statements for, while, if statements (13 min). mkv
5-5-Control Statements for, while, if statements (13 min). srt
5-6-Vectorization (14 min). mkv
5-6-Vectorization (14 min). srt
5-7-Working on and Submitting Programming Exercises (4 min). mkv
5-7-Working on and Submitting Programming Exercises (4 min). srt
6-1-Classification (8 min). mku
6-1-Classification (8 min). srt
6-2-Hypothesis Representation (7 min). mkv
6-2-Hypothesis Representation (7 min). srt
6-3-Decision Boundary (15 min). mkv
6-3-Decision Boundary (15 min). srt
6-4-Cost Function (11 min). mkv
6-4-Cost Function (11 min). srt
6-5-Simplified Cost Function and Gradient Descent (10 min). mkv
6-5-Simplified Cost Function and Gradient Descent (10 min). srt
6-6-Advanced Optimization (14 min). mkv
6-6-Advanced Optimization (14 min). srt
6-7-Multiclass Classification_One-vs-all (6 min). mkv
6-7-Multiclass Classification_One-vs-all (6 min). srt
7-1-The Problem of Overfitting (10 min). mky
7-1-The Problem of Overfitting (10 min). srt
7-2-Cost Function (10 min). mkv
7-2-Cost Function (10 min). srt
7-3-Regularized Linear Regression (11 min). mkv
7-3-Regularized Linear Regression (11 min). srt
7-4-Regularized Logistic Regression (9 min). mkv
7-4-Regularized Logistic Regression (9 min). srt
8-1-Non-linear Hypotheses (10 min). mkv
8-1-Non-linear Hypotheses (10 min). srt
8-2-Neurons and the Brain (8 min). mkv
8-2-Neurons and the Brain (8 min). srt
18-3-Model Representation I (12 min). mkv
8-3-Model Representation I (12 min). srt
8-4-Model Representation II (12 min). mkv
8-4-Model Representation II (12 min). srt
8-5-Examples and Intuitions I (7 min). mkv
8-5-Examples and Intuitions I (7 min). srt
8-6-Examples and Intuitions II (10 min). mkv
8-6-Examples and Intuitions II (10 min). srt
8-7-Multiclass Classification (4 min). mkv
8-7-Multiclass Classification (4 min). srt
9-1-Cost Function (7 min). mkv
9-1-Cost Function (7 min). srt
9-2-Backpropagation Algorithm (12 min). mkv
9-2-Backpropagation Algorithm (12 min). srt
9-3-Backpropagation Intuition (13 min). mkv
9-3-Backpropagation Intuition (13 min). srt
19-4-Implementation Note_Unrolling Parameters (8 min). mkv
9-4-Implementation Note_Unrolling Parameters (8 min). srt
9-5-Gradient Checking (12 min). mkv
9-5 -Gradient Checking (12 min). srt
9-6-Random Initialization (7 min). mkv9-6-Random Initialization (7 min). srt
9-7-Putting It Together (14 min). mkv
9-7 -Putting It Together (14 min). srt
9-8-Autonomous Driving (7 min). mkv
9-8 -Autonomous Driving (7 min). srt