[Python人工智能] 【470】机器学习-吴恩达[中文字幕]

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【470】机器学习-吴恩达[中文字幕]
【课程目录】
|   ├──1 - 1 - Welcome (7 min).mkv  11.69M
|   ├──1 - 1 - Welcome (7 min).srt  12.68kb
|   ├──1 - 2 - What is Machine Learning_ (7 min).mkv  9.25M
|   ├──1 - 2 - What is Machine Learning_ (7 min).srt  14.03kb
|   ├──1 - 3 - Supervised Learning (12 min).mkv  13.25M
|   ├──1 - 3 - Supervised Learning (12 min).srt  21.03kb
|   ├──1 - 4 - Unsupervised Learning (14 min).mkv  16.45M
|   ├──1 - 4 - Unsupervised Learning (14 min).srt  29.90kb
|   ├──2 - 1 - Model Representation (8 min).mkv  8.86M
|   ├──2 - 1 - Model Representation (8 min).srt  14.14kb
|   ├──2 - 2 - Cost Function (8 min).mkv  8.91M
|   ├──2 - 2 - Cost Function (8 min).srt  13.60kb
|   ├──2 - 3 - Cost Function - Intuition I (11 min).mkv  12.06M
|   ├──2 - 3 - Cost Function - Intuition I (11 min).srt  16.66kb
|   ├──2 - 4 - Cost Function - Intuition II (9 min).mkv  11.22M
|   ├──2 - 4 - Cost Function - Intuition II (9 min).srt  15.29kb
|   ├──2 - 5 - Gradient Descent (11 min).mkv  13.32M
|   ├──2 - 5 - Gradient Descent (11 min).srt  21.10kb
|   ├──2 - 6 - Gradient Descent Intuition (12 min).mkv  12.84M
|   ├──2 - 6 - Gradient Descent Intuition (12 min).srt  20.29kb
|   ├──2 - 7 - Gradient Descent For Linear Regression (10 min).srt  24.07kb
|   ├──2 - 7 - GradientDescentForLinearRegression  (6 min).mkv  12.02M
|   ├──2 - 8 - What_'s Next (6 min).mkv  5.99M
|   ├──2 - 8 - What_'s Next (6 min).srt  11.83kb
|   ├──3 - 1 - Matrices and Vectors (9 min).mkv  9.42M
|   ├──3 - 1 - Matrices and Vectors (9 min).srt  20.09kb
|   ├──3 - 2 - Addition and Scalar Multiplication (7 min).mkv  7.35M
|   ├──3 - 2 - Addition and Scalar Multiplication (7 min).srt  15.42kb
|   ├──3 - 3 - Matrix Vector Multiplication (14 min).mkv  14.78M
|   ├──3 - 3 - Matrix Vector Multiplication (14 min).srt  24.08kb
|   ├──3 - 4 - Matrix Matrix Multiplication (11 min).mkv  12.42M
|   ├──3 - 4 - Matrix Matrix Multiplication (11 min).srt  26.56kb
|   ├──3 - 5 - Matrix Multiplication Properties (9 min).mkv  9.67M
|   ├──3 - 5 - Matrix Multiplication Properties (9 min).srt  20.99kb
|   ├──3 - 6 - Inverse and Transpose (11 min).mkv  12.69M
|   ├──3 - 6 - Inverse and Transpose (11 min).srt  26.99kb
|   ├──4 - 1 - Multiple Features (8 min).mkv  8.71M
|   ├──4 - 1 - Multiple Features (8 min).srt  18.28kb
|   ├──4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv  5.71M
|   ├──4 - 2 - Gradient Descent for Multiple Variables (5 min).srt  11.93kb
|   ├──4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv  9.32M
|   ├──4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).srt  22.00kb
|   ├──4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv  9.13M
|   ├──4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).srt  23.38kb
|   ├──4 - 5 - Features and Polynomial Regression (8 min).mkv  8.15M
|   ├──4 - 5 - Features and Polynomial Regression (8 min).srt  20.72kb
|   ├──4 - 6 - Normal Equation (16 min).mkv  16.88M
|   ├──4 - 6 - Normal Equation (16 min).srt  43.11kb
|   ├──4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv  6.15M
|   ├──4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).srt  17.16kb
|   ├──5 - 1 - Basic Operations (14 min).mkv  17.50M
|   ├──5 - 1 - Basic Operations (14 min).srt  26.95kb
|   ├──5 - 2 - Moving Data Around (16 min).mkv  20.52M
|   ├──5 - 2 - Moving Data Around (16 min).srt  25.62kb
|   ├──5 - 3 - Computing on Data (13 min).mkv  15.04M
|   ├──5 - 3 - Computing on Data (13 min).srt  31.93kb
|   ├──5 - 4 - Plotting Data (10 min).mkv  13.17M
|   ├──5 - 4 - Plotting Data (10 min).srt  21.98kb
|   ├──5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  16.29M
|   ├──5 - 5 - Control Statements_ for, while, if statements (13 min).srt  30.77kb
|   ├──5 - 6 - Vectorization (14 min).mkv  15.88M
|   ├──5 - 6 - Vectorization (14 min).srt  25.93kb
|   ├──5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv  5.41M
|   ├──5 - 7 - Working on and Submitting Programming Exercises (4 min).srt  7.76kb
|   ├──6 - 1 - Classification (8 min).mkv  8.65M
|   ├──6 - 1 - Classification (8 min).srt  20.93kb
|   ├──6 - 2 - Hypothesis Representation (7 min).mkv  8.23M
|   ├──6 - 2 - Hypothesis Representation (7 min).srt  17.82kb
|   ├──6 - 3 - Decision Boundary (15 min).mkv  16.51M
|   ├──6 - 3 - Decision Boundary (15 min).srt  25.48kb
|   ├──6 - 4 - Cost Function (11 min).mkv  12.92M
|   ├──6 - 4 - Cost Function (11 min).srt  27.66kb
|   ├──6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv  11.80M
|   ├──6 - 5 - Simplified Cost Function and Gradient Descent (10 min).srt  25.19kb
|   ├──6 - 6 - Advanced Optimization (14 min).mkv  17.95M
|   ├──6 - 6 - Advanced Optimization (14 min).srt  28.89kb
|   ├──6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv  6.83M
|   ├──6 - 7 - Multiclass Classification_ One-vs-all (6 min).srt  16.31kb
|   ├──7 - 1 - The Problem of Overfitting (10 min).mkv  11.00M
|   ├──7 - 1 - The Problem of Overfitting (10 min).srt  24.69kb
|   ├──7 - 2 - Cost Function (10 min).mkv  11.48M
|   ├──7 - 2 - Cost Function (10 min).srt  25.81kb
|   ├──7 - 3 - Regularized Linear Regression (11 min).mkv  11.84M
|   ├──7 - 3 - Regularized Linear Regression (11 min).srt  26.22kb
|   ├──7 - 4 - Regularized Logistic Regression (9 min).mkv  10.77M
|   ├──7 - 4 - Regularized Logistic Regression (9 min).srt  22.21kb
|   ├──8 - 1 - Non-linear Hypotheses (10 min).mkv  10.73M
|   ├──8 - 1 - Non-linear Hypotheses (10 min).srt  24.54kb
|   ├──8 - 2 - Neurons and the Brain (8 min).mkv  9.77M
|   ├──8 - 2 - Neurons and the Brain (8 min).srt  21.07kb
|   ├──8 - 3 - Model Representation I (12 min).mkv  13.32M
|   ├──8 - 3 - Model Representation I (12 min).srt  25.42kb
|   ├──8 - 4 - Model Representation II (12 min).mkv  13.27M
|   ├──8 - 4 - Model Representation II (12 min).srt  26.62kb
|   ├──8 - 5 - Examples and Intuitions I (7 min).mkv  7.78M
|   ├──8 - 5 - Examples and Intuitions I (7 min).srt  16.32kb
|   ├──8 - 6 - Examples and Intuitions II (10 min).mkv  13.84M
|   ├──8 - 6 - Examples and Intuitions II (10 min).srt  22.02kb
|   ├──8 - 7 - Multiclass Classification (4 min).mkv  4.77M
|   ├──8 - 7 - Multiclass Classification (4 min).srt  9.65kb
|   ├──9 - 1 - Cost Function (7 min).mkv  7.56M
|   ├──9 - 1 - Cost Function (7 min).srt  16.85kb
|   ├──9 - 2 - Backpropagation Algorithm (12 min).mkv  13.75M
|   ├──9 - 2 - Backpropagation Algorithm (12 min).srt  27.03kb
|   ├──9 - 3 - Backpropagation Intuition (13 min).mkv  15.25M
|   ├──9 - 3 - Backpropagation Intuition (13 min).srt  27.82kb
|   ├──9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv  9.27M
|   ├──9 - 4 - Implementation Note_ Unrolling Parameters (8 min).srt  19.35kb
|   ├──9 - 5 - Gradient Checking (12 min).mkv  13.32M
|   ├──9 - 5 - Gradient Checking (12 min).srt  28.52kb
|   ├──9 - 6 - Random Initialization (7 min).mkv  7.46M
|   ├──9 - 6 - Random Initialization (7 min).srt  17.45kb
|   ├──9 - 7 - Putting It Together (14 min).mkv  16.10M
|   ├──9 - 7 - Putting It Together (14 min).srt  29.92kb
|   ├──9 - 8 - Autonomous Driving (7 min).mkv  14.79M
|   ├──9 - 8 - Autonomous Driving (7 min).srt  12.80kb
|   ├──10 - 1 - Deciding What to Try Next (6 min).mkv  6.78M
|   ├──10 - 1 - Deciding What to Try Next (6 min).srt  16.10kb
|   ├──10 - 2 - Evaluating a Hypothesis (8 min).mkv  8.36M
|   ├──10 - 2 - Evaluating a Hypothesis (8 min).srt  15.07kb
|   ├──10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv  14.92M
|   ├──10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).srt  11.80kb
|   ├──10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv  8.86M
|   ├──10 - 4 - Diagnosing Bias vs. Variance (8 min).srt  16.04kb
|   ├──10 - 5 - Regularization and Bias_Variance (11 min).mkv  12.42M
|   ├──10 - 5 - Regularization and Bias_Variance (11 min).srt  28.53kb
|   ├──10 - 6 - Learning Curves (12 min).mkv  12.74M
|   ├──10 - 6 - Learning Curves (12 min).srt  30.41kb
|   ├──10 - 7 - Deciding What to Do Next Revisited (7 min).mkv  8.08M
|   ├──10 - 7 - Deciding What to Do Next Revisited (7 min).srt  18.40kb
|   ├──11 - 1 - Prioritizing What to Work On (10 min).mkv  11.03M
|   ├──11 - 1 - Prioritizing What to Work On (10 min).srt  25.35kb
|   ├──11 - 2 - Error Analysis (13 min).mkv  15.22M
|   ├──11 - 2 - Error Analysis (13 min).srt  29.57kb
|   ├──11 - 3 - Error Metrics for Skewed Classes (12 min).mkv  13.07M
|   ├──11 - 3 - Error Metrics for Skewed Classes (12 min).srt  27.00kb
|   ├──11 - 4 - Trading Off Precision and Recall (14 min).mkv  15.77M
|   ├──11 - 4 - Trading Off Precision and Recall (14 min).srt  28.80kb
|   ├──11 - 5 - Data For Machine Learning (11 min).mkv  12.70M
|   ├──11 - 5 - Data For Machine Learning (11 min).srt  29.97kb
|   ├──12 - 1 - Optimization Objective (15 min).mkv  16.42M
|   ├──12 - 1 - Optimization Objective (15 min).srt  28.01kb
|   ├──12 - 2 - Large Margin Intuition (11 min).mkv  11.65M
|   ├──12 - 2 - Large Margin Intuition (11 min).srt  26.67kb
|   ├──12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv  21.51M
|   ├──12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).srt  25.07kb
|   ├──12 - 4 - Kernels I (16 min).mkv  17.32M
|   ├──12 - 4 - Kernels I (16 min).srt  36.72kb
|   ├──12 - 5 - Kernels II (16 min).mkv  17.20M
|   ├──12 - 5 - Kernels II (16 min).srt  26.99kb
|   ├──12 - 6 - Using An SVM (21 min).mkv  23.63M
|   ├──12 - 6 - Using An SVM (21 min).srt  29.21kb
|   ├──13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv  3.76M
|   ├──13 - 1 - Unsupervised Learning_ Introduction (3 min).srt  9.13kb
|   ├──13 - 2 - K-Means Algorithm (13 min).mkv  13.61M
|   ├──13 - 2 - K-Means Algorithm (13 min).srt  29.35kb
|   ├──13 - 3 - Optimization Objective (7 min)(1).mkv  8.04M
|   ├──13 - 3 - Optimization Objective (7 min).mkv  8.03M
|   ├──13 - 3 - Optimization Objective (7 min).srt  17.31kb
|   ├──13 - 4 - Random Initialization (8 min).mkv  8.56M
|   ├──13 - 4 - Random Initialization (8 min).srt  20.96kb
|   ├──13 - 5 - Choosing the Number of Clusters (8 min).mkv  9.28M
|   ├──13 - 5 - Choosing the Number of Clusters (8 min).srt  23.20kb
|   ├──14 - 1 - Motivation I_ Data Compression (10 min).mkv  14.15M
|   ├──14 - 1 - Motivation I_ Data Compression (10 min).srt  25.76kb
|   ├──14 - 2 - Motivation II_ Visualization (6 min).mkv  6.22M
|   ├──14 - 2 - Motivation II_ Visualization (6 min).srt  13.04kb
|   ├──14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv  10.32M
|   ├──14 - 3 - Principal Component Analysis Problem Formulation (9 min).srt  23.55kb
|   ├──14 - 4 - Principal Component Analysis Algorithm (15 min).mkv  17.55M
|   ├──14 - 4 - Principal Component Analysis Algorithm (15 min).srt  36.35kb
|   ├──14 - 5 - Choosing the Number of Principal Components (11 min).mkv  11.67M
|   ├──14 - 5 - Choosing the Number of Principal Components (11 min).srt  26.82kb
|   ├──14 - 6 - Reconstruction from Compressed Representation (4 min).mkv  4.92M
|   ├──14 - 6 - Reconstruction from Compressed Representation (4 min).srt  9.56kb
|   ├──14 - 7 - Advice for Applying PCA (13 min).mkv  14.50M
|   ├──14 - 7 - Advice for Applying PCA (13 min).srt  33.54kb
|   ├──15 - 1 - Problem Motivation (8 min).mkv  8.23M
|   ├──15 - 1 - Problem Motivation (8 min).srt  20.53kb
|   ├──15 - 2 - Gaussian Distribution (10 min).mkv  11.53M
|   ├──15 - 2 - Gaussian Distribution (10 min).srt  25.91kb
|   ├──15 - 3 - Algorithm (12 min).mkv  13.77M
|   ├──15 - 3 - Algorithm (12 min).srt  28.13kb
|   ├──15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv  14.96M
|   ├──15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).srt  29.80kb
|   ├──15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv  9.17M
|   ├──15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).srt  21.69kb
|   ├──15 - 6 - Choosing What Features to Use (12 min).mkv  13.92M
|   ├──15 - 6 - Choosing What Features to Use (12 min).srt  33.31kb
|   ├──15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv  15.72M
|   ├──15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).srt  29.61kb
|   ├──15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv  16.12M
|   ├──15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).srt  34.14kb
|   ├──16 - 1 - Problem Formulation (8 min).mkv  10.57M
|   ├──16 - 1 - Problem Formulation (8 min).srt  21.54kb
|   ├──16 - 2 - Content Based Recommendations (15 min).mkv  16.71M
|   ├──16 - 2 - Content Based Recommendations (15 min).srt  35.81kb
|   ├──16 - 3 - Collaborative Filtering (10 min).mkv  11.60M
|   ├──16 - 3 - Collaborative Filtering (10 min).srt  26.03kb
|   ├──16 - 4 - Collaborative Filtering Algorithm (9 min).mkv  10.18M
|   ├──16 - 4 - Collaborative Filtering Algorithm (9 min).srt  21.07kb
|   ├──16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv  9.55M
|   ├──16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).srt  20.93kb
|   ├──16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv  9.58M
|   ├──16 - 6 - Implementational Detail_ Mean Normalization (9 min).srt  21.37kb
|   ├──17 - 1 - Learning With Large Datasets (6 min).mkv  6.41M
|   ├──17 - 1 - Learning With Large Datasets (6 min).srt  10.87kb
|   ├──17 - 2 - Stochastic Gradient Descent (13 min).mkv  15.12M
|   ├──17 - 2 - Stochastic Gradient Descent (13 min).srt  24.77kb
|   ├──17 - 3 - Mini-Batch Gradient Descent (6 min).mkv  7.22M
|   ├──17 - 3 - Mini-Batch Gradient Descent (6 min).srt  11.03kb
|   ├──17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv  13.15M
|   ├──17 - 4 - Stochastic Gradient Descent Convergence (12 min).srt  21.93kb
|   ├──17 - 5 - Online Learning (13 min).mkv  14.72M
|   ├──17 - 5 - Online Learning (13 min).srt  36.40kb
|   ├──17 - 6 - Map Reduce and Data Parallelism (14 min).mkv  15.84M
|   ├──17 - 6 - Map Reduce and Data Parallelism (14 min).srt  28.94kb
|   ├──18 - 1 - Problem Description and Pipeline (7 min).mkv  7.81M
|   ├──18 - 1 - Problem Description and Pipeline (7 min).srt  19.20kb
|   ├──18 - 2 - Sliding Windows (15 min).mkv  16.30M
|   ├──18 - 2 - Sliding Windows (15 min).srt  39.58kb
|   ├──18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv  18.57M
|   ├──18 - 3 - Getting Lots of Data and Artificial Data (16 min).srt  46.16kb
|   ├──18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv  15.90M
|   ├──18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).srt  31.63kb
|   ├──19 - 1 - Summary and Thank You (5 min).mkv  6.02M
|   ├──19 - 1 - Summary and Thank You (5 min).srt  10.81kb
|   ├──缺中文字幕的视频.txt  0.09kb
|   ├──如何添加中文字幕.docx  120.33kb
|   └──中英文字幕【猴哥教程网www.hougejiaocheng.com分享】.rar  43.32kb

|   ├──代码资料及PDF
|   ├──教程和笔记  
|   |   ├──机器学习个人笔记完整版2.5.pdf  8.18M
|   |   └──机器学习个人笔记完整版2.5_Kindle7寸(1).pdf  6.66M

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