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种子哈希：e9d6c0d130949e16f3f8d7105241d28b55590a18
文档大小：1.6 GB
文档个数：274个文档
下载次数：9549次
下载速度：极快
收录时间：2020-01-25
最近下载：2024-10-08
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文档列表

VIII. Neural Networks Representation (Week 4)/docs_slides_Lecture8.pptx 42.3 MB
XII. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).mp4 25.1 MB
XII. Support Vector Machines (Week 7)/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4 22.9 MB
V. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).mp4 21.8 MB
XVIII. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4 19.7 MB
VI. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).mp4 19.0 MB
XIV. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).mp4 18.7 MB
V. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).mp4 18.6 MB
XII. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).mp4 18.4 MB
XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min) (1).mp4 18.3 MB
XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).mp4 18.3 MB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).mp4 18.0 MB
XVI. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).mp4 17.8 MB
VI. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).mp4 17.6 MB
I. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).mp4 17.5 MB
XII. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).mp4 17.5 MB
XVIII. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).mp4 17.3 MB
V. Octave Tutorial (Week 2)/5 - 5 - Control Statements for while if statements (13 min).mp4 17.3 MB
XV. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4 17.1 MB
IX. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).mp4 17.1 MB
XVIII. Application Example Photo OCR/18 - 4 - Ceiling Analysis What Part of the Pipeline to Work on Next (14 min).mp4 16.9 MB
V. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).mp4 16.9 MB
XVII. Large Scale Machine Learning (Week 10)/17 - 6 - Map Reduce and Data Parallelism (14 min).mp4 16.8 MB
XI. Machine Learning System Design (Week 6)/11 - 4 - Trading Off Precision and Recall (14 min).mp4 16.8 MB
XV. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4 16.7 MB
IX. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).mp4 16.2 MB
XI. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).mp4 16.2 MB
XVII. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).mp4 16.1 MB
V. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).mp4 16.0 MB
XV. Anomaly Detection (Week 9)/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4 15.9 MB
III. Linear Algebra Review (Week 1, Optional)/3 - 3 - Matrix Vector Multiplication (14 min).mp4 15.7 MB
XVII. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).mp4 15.6 MB
IX. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).mp4 15.6 MB
XIV. Dimensionality Reduction (Week 8)/14 - 7 - Advice for Applying PCA (13 min).mp4 15.4 MB
XIV. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I Data Compression (10 min).mp4 15.0 MB
XV. Anomaly Detection (Week 9)/15 - 6 - Choosing What Features to Use (12 min).mp4 14.8 MB
X. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mp4 14.8 MB
VIII. Neural Networks Representation (Week 4)/8 - 6 - Examples and Intuitions II (10 min).mp4 14.7 MB
XV. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).mp4 14.6 MB
IX. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).mp4 14.6 MB
XIII. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).mp4 14.5 MB
VIII. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).mp4 14.2 MB
II. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).mp4 14.2 MB
IX. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).mp4 14.2 MB
I. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).mp4 14.1 MB
VIII. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).mp4 14.1 MB
XVII. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4 14.0 MB
V. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).mp4 14.0 MB
XI. Machine Learning System Design (Week 6)/11 - 3 - Error Metrics for Skewed Classes (12 min).mp4 13.9 MB
VI. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).mp4 13.7 MB
II. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).mp4 13.7 MB
X. Advice for Applying Machine Learning (Week 6)/10 - 6 - Learning Curves (12 min).mp4 13.5 MB
XI. Machine Learning System Design (Week 6)/11 - 5 - Data For Machine Learning (11 min).mp4 13.5 MB
III. Linear Algebra Review (Week 1, Optional)/3 - 6 - Inverse and Transpose (11 min).mp4 13.5 MB
X. Advice for Applying Machine Learning (Week 6)/10 - 5 - Regularization and Bias_Variance (11 min).mp4 13.2 MB
III. Linear Algebra Review (Week 1, Optional)/3 - 4 - Matrix Matrix Multiplication (11 min).mp4 13.2 MB
II. Linear Regression with One Variable (Week 1)/2 - 3 - Cost Function - Intuition I (11 min).mp4 12.8 MB
II. Linear Regression with One Variable (Week 1)/2 - 7 - Gradient Descent For Linear Regression (10 min).mp4 12.8 MB
VII. Regularization (Week 3)/7 - 3 - Regularized Linear Regression (11 min).mp4 12.6 MB
VI. Logistic Regression (Week 3)/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp4 12.5 MB
I. Introduction (Week 1)/1 - 1 - Welcome (7 min).mp4 12.5 MB
XIV. Dimensionality Reduction (Week 8)/14 - 5 - Choosing the Number of Principal Components (11 min).mp4 12.4 MB
XII. Support Vector Machines (Week 7)/12 - 2 - Large Margin Intuition (11 min).mp4 12.4 MB
XVI. Recommender Systems (Week 9)/16 - 3 - Collaborative Filtering (10 min).mp4 12.3 MB
XV. Anomaly Detection (Week 9)/15 - 2 - Gaussian Distribution (10 min).mp4 12.3 MB
VII. Regularization (Week 3)/7 - 2 - Cost Function (10 min).mp4 12.2 MB
II. Linear Regression with One Variable (Week 1)/2 - 4 - Cost Function - Intuition II (9 min).mp4 11.9 MB
XI. Machine Learning System Design (Week 6)/11 - 1 - Prioritizing What to Work On (10 min).mp4 11.7 MB
VII. Regularization (Week 3)/7 - 1 - The Problem of Overfitting (10 min).mp4 11.7 MB
XIV. Dimensionality Reduction (Week 8)/ex7.zip 11.6 MB
VII. Regularization (Week 3)/7 - 4 - Regularized Logistic Regression (9 min).mp4 11.4 MB
VIII. Neural Networks Representation (Week 4)/8 - 1 - Non-linear Hypotheses (10 min).mp4 11.4 MB
XVI. Recommender Systems (Week 9)/16 - 1 - Problem Formulation (8 min).mp4 11.2 MB
XIV. Dimensionality Reduction (Week 8)/14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp4 11.0 MB
XVI. Recommender Systems (Week 9)/16 - 4 - Collaborative Filtering Algorithm (9 min).mp4 10.8 MB
VIII. Neural Networks Representation (Week 4)/8 - 2 - Neurons and the Brain (8 min).mp4 10.4 MB
III. Linear Algebra Review (Week 1, Optional)/3 - 5 - Matrix Multiplication Properties (9 min).mp4 10.3 MB
XVI. Recommender Systems (Week 9)/16 - 6 - Implementational Detail Mean Normalization (9 min).mp4 10.2 MB
XVI. Recommender Systems (Week 9)/16 - 5 - Vectorization Low Rank Matrix Factorization (8 min).mp4 10.2 MB
III. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).mp4 10.0 MB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp4 9.9 MB
XIII. Clustering (Week 8)/13 - 5 - Choosing the Number of Clusters (8 min).mp4 9.9 MB
IX. Neural Networks Learning (Week 5)/9 - 4 - Implementation Note Unrolling Parameters (8 min).mp4 9.8 MB
I. Introduction (Week 1)/1 - 2 - What is Machine Learning (7 min).mp4 9.8 MB
XV. Anomaly Detection (Week 9)/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp4 9.7 MB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp4 9.7 MB
II. Linear Regression with One Variable (Week 1)/2 - 2 - Cost Function (8 min).mp4 9.5 MB
II. Linear Regression with One Variable (Week 1)/2 - 1 - Model Representation (8 min).mp4 9.4 MB
X. Advice for Applying Machine Learning (Week 6)/10 - 4 - Diagnosing Bias vs. Variance (8 min).mp4 9.4 MB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 1 - Multiple Features (8 min).mp4 9.3 MB
VI. Logistic Regression (Week 3)/6 - 1 - Classification (8 min).mp4 9.2 MB
XIII. Clustering (Week 8)/13 - 4 - Random Initialization (8 min).mp4 9.1 MB
X. Advice for Applying Machine Learning (Week 6)/10 - 2 - Evaluating a Hypothesis (8 min).mp4 8.9 MB
XV. Anomaly Detection (Week 9)/15 - 1 - Problem Motivation (8 min).mp4 8.8 MB
VI. Logistic Regression (Week 3)/6 - 2 - Hypothesis Representation (7 min).mp4 8.7 MB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 5 - Features and Polynomial Regression (8 min).mp4 8.7 MB
X. Advice for Applying Machine Learning (Week 6)/10 - 7 - Deciding What to Do Next Revisited (7 min).mp4 8.6 MB
XIII. Clustering (Week 8)/13 - 3 - Optimization Objective (7 min).mp4 8.5 MB
XVIII. Application Example Photo OCR/18 - 1 - Problem Description and Pipeline (7 min).mp4 8.3 MB
VIII. Neural Networks Representation (Week 4)/8 - 5 - Examples and Intuitions I (7 min).mp4 8.3 MB
IX. Neural Networks Learning (Week 5)/9 - 1 - Cost Function (7 min).mp4 8.0 MB
IX. Neural Networks Learning (Week 5)/ex4.zip 7.9 MB
IX. Neural Networks Learning (Week 5)/9 - 6 - Random Initialization (7 min).mp4 7.9 MB
VIII. Neural Networks Representation (Week 4)/ex3.zip 7.9 MB
III. Linear Algebra Review (Week 1, Optional)/3 - 2 - Addition and Scalar Multiplication (7 min).mp4 7.8 MB
XVII. Large Scale Machine Learning (Week 10)/17 - 3 - Mini-Batch Gradient Descent (6 min).mp4 7.7 MB
VI. Logistic Regression (Week 3)/6 - 7 - Multiclass Classification One-vs-all (6 min).mp4 7.3 MB
X. Advice for Applying Machine Learning (Week 6)/10 - 1 - Deciding What to Try Next (6 min).mp4 7.2 MB
XVII. Large Scale Machine Learning (Week 10)/17 - 1 - Learning With Large Datasets (6 min).mp4 6.8 MB
XIV. Dimensionality Reduction (Week 8)/14 - 2 - Motivation II Visualization (6 min).mp4 6.6 MB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp4 6.5 MB
XVIII. Application Example Photo OCR/docs_slides_Lecture18.pptx 6.4 MB
XIX. Conclusion/19 - 1 - Summary and Thank You (5 min).mp4 6.4 MB
II. Linear Regression with One Variable (Week 1)/2 - 8 - Whats Next (6 min).mp4 6.4 MB
XV. Anomaly Detection (Week 9)/docs_slides_Lecture15.pptx 6.3 MB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 2 - Gradient Descent for Multiple Variables (5 min).mp4 6.1 MB
V. Octave Tutorial (Week 2)/5 - 7 - Working on and Submitting Programming Exercises (4 min).mp4 5.7 MB
XII. Support Vector Machines (Week 7)/docs_slides_Lecture12.pptx 5.6 MB
II. Linear Regression with One Variable (Week 1)/docs_slides_Lecture2.pptx 5.6 MB
XIV. Dimensionality Reduction (Week 8)/14 - 6 - Reconstruction from Compressed Representation (4 min).mp4 5.2 MB
VIII. Neural Networks Representation (Week 4)/docs_slides_Lecture8.pdf 5.2 MB
IX. Neural Networks Learning (Week 5)/docs_slides_Lecture9.pptx 5.2 MB
III. Linear Algebra Review (Week 1, Optional)/docs_slides_Lecture3.pptx 5.2 MB
VIII. Neural Networks Representation (Week 4)/8 - 7 - Multiclass Classification (4 min).mp4 5.1 MB
IV. Linear Regression with Multiple Variables (Week 2)/docs_slides_Lecture4.pptx 4.6 MB
I. Introduction (Week 1)/docs_slides_Lecture1.pptx 4.2 MB
VI. Logistic Regression (Week 3)/docs_slides_Lecture6.pptx 4.0 MB
XIII. Clustering (Week 8)/13 - 1 - Unsupervised Learning Introduction (3 min).mp4 4.0 MB
XVII. Large Scale Machine Learning (Week 10)/docs_slides_Lecture17.pptx 4.0 MB
XIV. Dimensionality Reduction (Week 8)/docs_slides_Lecture14.pptx 3.8 MB
XVI. Recommender Systems (Week 9)/docs_slides_Lecture16.pptx 3.8 MB
IX. Neural Networks Learning (Week 5)/docs_slides_Lecture9.pdf 3.5 MB
X. Advice for Applying Machine Learning (Week 6)/docs_slides_Lecture10.pptx 3.5 MB
XV. Anomaly Detection (Week 9)/docs_slides_Lecture15.pdf 3.5 MB
I. Introduction (Week 1)/docs_slides_Lecture1.pdf 3.5 MB
II. Linear Regression with One Variable (Week 1)/docs_slides_Lecture2.pdf 3.0 MB
XIII. Clustering (Week 8)/docs_slides_Lecture13.pptx 2.9 MB
VII. Regularization (Week 3)/docs_slides_Lecture7.pptx 2.7 MB
VII. Regularization (Week 3)/docs_slides_Lecture7.pdf 2.5 MB
XII. Support Vector Machines (Week 7)/docs_slides_Lecture12.pdf 2.4 MB
XIII. Clustering (Week 8)/docs_slides_Lecture13.pdf 2.3 MB
VI. Logistic Regression (Week 3)/docs_slides_Lecture6.pdf 2.2 MB
XVII. Large Scale Machine Learning (Week 10)/docs_slides_Lecture17.pdf 2.1 MB
XVIII. Application Example Photo OCR/docs_slides_Lecture18.pdf 2.1 MB
XI. Machine Learning System Design (Week 6)/docs_slides_Lecture11.pptx 2.0 MB
III. Linear Algebra Review (Week 1, Optional)/docs_slides_Lecture3.pdf 1.9 MB
IV. Linear Regression with Multiple Variables (Week 2)/docs_slides_Lecture4.pdf 1.8 MB
XIV. Dimensionality Reduction (Week 8)/docs_slides_Lecture14.pdf 1.7 MB
X. Advice for Applying Machine Learning (Week 6)/docs_slides_Lecture10.pdf 1.6 MB
XVI. Recommender Systems (Week 9)/docs_slides_Lecture16.pdf 1.5 MB
XII. Support Vector Machines (Week 7)/ex6.zip 917.9 kB
XVI. Recommender Systems (Week 9)/ex8.zip 813.9 kB
XI. Machine Learning System Design (Week 6)/docs_slides_Lecture11.pdf 509.6 kB
IV. Linear Regression with Multiple Variables (Week 2)/ex1.zip 481.1 kB
V. Octave Tutorial (Week 2)/docs_slides_Lecture5.pptx 417.1 kB
VII. Regularization (Week 3)/ex2.zip 248.8 kB
V. Octave Tutorial (Week 2)/docs_slides_Lecture5.pdf 248.2 kB
X. Advice for Applying Machine Learning (Week 6)/ex5.zip 181.3 kB
avatar.png 56.8 kB
XII. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).srt 44.5 kB
XII. Support Vector Machines (Week 7)/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).srt 36.7 kB
XVIII. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).srt 36.0 kB
XVIII. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).srt 32.2 kB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).srt 31.9 kB
XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min) (1).srt 31.4 kB
XII. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).srt 31.4 kB
XVIII. Application Example Photo OCR/18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).srt 31.2 kB
XII. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).srt 30.1 kB
XII. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).srt 29.8 kB
I. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).srt 29.8 kB
XVII. Large Scale Machine Learning (Week 10)/17 - 6 - Map Reduce and Data Parallelism (14 min).srt 29.6 kB
V. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).srt 29.3 kB
XI. Machine Learning System Design (Week 6)/11 - 4 - Trading Off Precision and Recall (14 min).srt 29.3 kB
XIV. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).srt 29.3 kB
XVI. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).srt 29.3 kB
VI. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).srt 28.5 kB
IX. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).srt 28.3 kB
XVII. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).srt 28.3 kB
XI. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).srt 28.1 kB
XV. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).srt 28.1 kB
XV. Anomaly Detection (Week 9)/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).srt 27.9 kB
VI. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).srt 27.4 kB
XIV. Dimensionality Reduction (Week 8)/14 - 7 - Advice for Applying PCA (13 min).srt 27.0 kB
XV. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).srt 26.9 kB
XIII. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).srt 26.9 kB
V. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).srt 26.0 kB
V. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).srt 25.8 kB
XV. Anomaly Detection (Week 9)/15 - 6 - Choosing What Features to Use (12 min).srt 25.7 kB
IX. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).srt 25.6 kB
V. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).srt 25.5 kB
X. Advice for Applying Machine Learning (Week 6)/10 - 6 - Learning Curves (12 min).srt 25.3 kB
X. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).srt 25.2 kB
III. Linear Algebra Review (Week 1, Optional)/3 - 3 - Matrix Vector Multiplication (14 min).srt 24.8 kB
IX. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).srt 24.1 kB
XV. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).srt 24.1 kB
V. Octave Tutorial (Week 2)/5 - 5 - Control Statements- for, while, if statements (13 min).srt 23.9 kB
XI. Machine Learning System Design (Week 6)/11 - 5 - Data For Machine Learning (11 min).srt 23.7 kB
IX. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).srt 23.4 kB
X. Advice for Applying Machine Learning (Week 6)/10 - 5 - Regularization and Bias_Variance (11 min).srt 23.1 kB
VIII. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).srt 23.0 kB
VI. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).srt 22.7 kB
XI. Machine Learning System Design (Week 6)/11 - 3 - Error Metrics for Skewed Classes (12 min).srt 22.6 kB
VIII. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).srt 22.1 kB
XII. Support Vector Machines (Week 7)/12 - 2 - Large Margin Intuition (11 min).srt 21.8 kB
XIV. Dimensionality Reduction (Week 8)/14 - 5 - Choosing the Number of Principal Components (11 min).srt 21.7 kB
III. Linear Algebra Review (Week 1, Optional)/3 - 6 - Inverse and Transpose (11 min).srt 21.6 kB
XV. Anomaly Detection (Week 9)/15 - 2 - Gaussian Distribution (10 min).srt 21.1 kB
III. Linear Algebra Review (Week 1, Optional)/3 - 4 - Matrix Matrix Multiplication (11 min).srt 21.1 kB
VII. Regularization (Week 3)/7 - 3 - Regularized Linear Regression (11 min).srt 20.9 kB
XVI. Recommender Systems (Week 9)/16 - 3 - Collaborative Filtering (10 min).srt 20.7 kB
XIV. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I- Data Compression (10 min).srt 20.6 kB
VII. Regularization (Week 3)/7 - 2 - Cost Function (10 min).srt 20.2 kB
XI. Machine Learning System Design (Week 6)/11 - 1 - Prioritizing What to Work On (10 min).srt 20.1 kB
VI. Logistic Regression (Week 3)/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).srt 20.0 kB
VII. Regularization (Week 3)/7 - 1 - The Problem of Overfitting (10 min).srt 19.7 kB
VIII. Neural Networks Representation (Week 4)/8 - 1 - Non-linear Hypotheses (10 min).srt 19.5 kB
II. Linear Regression with One Variable (Week 1)/2 - 7 - Gradient Descent For Linear Regression (10 min).srt 19.2 kB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).srt 18.9 kB
XIV. Dimensionality Reduction (Week 8)/14 - 3 - Principal Component Analysis Problem Formulation (9 min).srt 18.9 kB
XVII. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).srt 18.6 kB
XIII. Clustering (Week 8)/13 - 5 - Choosing the Number of Clusters (8 min).srt 18.4 kB
V. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).srt 17.8 kB
VII. Regularization (Week 3)/7 - 4 - Regularized Logistic Regression (9 min).srt 17.6 kB
VIII. Neural Networks Representation (Week 4)/8 - 6 - Examples and Intuitions II (10 min).srt 17.5 kB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).srt 17.4 kB
I. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).srt 17.2 kB
XVI. Recommender Systems (Week 9)/16 - 1 - Problem Formulation (8 min).srt 17.2 kB
III. Linear Algebra Review (Week 1, Optional)/3 - 5 - Matrix Multiplication Properties (9 min).srt 17.2 kB
XVI. Recommender Systems (Week 9)/16 - 6 - Implementational Detail- Mean Normalization (9 min).srt 17.0 kB
XVI. Recommender Systems (Week 9)/16 - 4 - Collaborative Filtering Algorithm (9 min).srt 16.9 kB
XV. Anomaly Detection (Week 9)/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).srt 16.8 kB
VIII. Neural Networks Representation (Week 4)/8 - 2 - Neurons and the Brain (8 min).srt 16.8 kB
XVI. Recommender Systems (Week 9)/16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).srt 16.7 kB
XIII. Clustering (Week 8)/13 - 4 - Random Initialization (8 min).srt 16.6 kB
VI. Logistic Regression (Week 3)/6 - 1 - Classification (8 min).srt 16.6 kB
XVII. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).srt 16.6 kB
X. Advice for Applying Machine Learning (Week 6)/10 - 4 - Diagnosing Bias vs. Variance (8 min).srt 16.5 kB
XV. Anomaly Detection (Week 9)/15 - 1 - Problem Motivation (8 min).srt 16.4 kB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 5 - Features and Polynomial Regression (8 min).srt 16.3 kB
III. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).srt 16.3 kB
II. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).srt 15.9 kB
II. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).srt 15.8 kB
IX. Neural Networks Learning (Week 5)/9 - 4 - Implementation Note- Unrolling Parameters (8 min).srt 15.3 kB
XVIII. Application Example Photo OCR/18 - 1 - Problem Description and Pipeline (7 min).srt 15.1 kB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 1 - Multiple Features (8 min).srt 14.9 kB
VI. Logistic Regression (Week 3)/6 - 2 - Hypothesis Representation (7 min).srt 14.5 kB
X. Advice for Applying Machine Learning (Week 6)/10 - 7 - Deciding What to Do Next Revisited (7 min).srt 14.4 kB
IX. Neural Networks Learning (Week 5)/9 - 6 - Random Initialization (7 min).srt 14.3 kB
XIII. Clustering (Week 8)/13 - 3 - Optimization Objective (7 min).srt 14.0 kB
IX. Neural Networks Learning (Week 5)/9 - 1 - Cost Function (7 min).srt 13.5 kB
VIII. Neural Networks Representation (Week 4)/8 - 5 - Examples and Intuitions I (7 min).srt 13.4 kB
VI. Logistic Regression (Week 3)/6 - 7 - Multiclass Classification- One-vs-all (6 min).srt 12.9 kB
X. Advice for Applying Machine Learning (Week 6)/10 - 1 - Deciding What to Try Next (6 min).srt 12.7 kB
II. Linear Regression with One Variable (Week 1)/2 - 3 - Cost Function - Intuition I (11 min).srt 12.4 kB
III. Linear Algebra Review (Week 1, Optional)/3 - 2 - Addition and Scalar Multiplication (7 min).srt 12.3 kB
X. Advice for Applying Machine Learning (Week 6)/10 - 2 - Evaluating a Hypothesis (8 min).srt 11.8 kB
II. Linear Regression with One Variable (Week 1)/2 - 4 - Cost Function - Intuition II (9 min).srt 11.4 kB
XIV. Dimensionality Reduction (Week 8)/14 - 2 - Motivation II- Visualization (6 min).srt 10.4 kB
I. Introduction (Week 1)/1 - 2 - What is Machine Learning- (7 min).srt 10.4 kB
II. Linear Regression with One Variable (Week 1)/2 - 1 - Model Representation (8 min).srt 10.2 kB
I. Introduction (Week 1)/1 - 1 - Welcome (7 min).srt 10.1 kB
II. Linear Regression with One Variable (Week 1)/2 - 2 - Cost Function (8 min).srt 10.1 kB
IX. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).srt 10.0 kB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).srt 10.0 kB
II. Linear Regression with One Variable (Week 1)/2 - 8 - What's Next (6 min).srt 8.7 kB
XIX. Conclusion/19 - 1 - Summary and Thank You (5 min).srt 8.3 kB
XVII. Large Scale Machine Learning (Week 10)/17 - 1 - Learning With Large Datasets (6 min).srt 8.1 kB
XVII. Large Scale Machine Learning (Week 10)/17 - 3 - Mini-Batch Gradient Descent (6 min).srt 8.0 kB
XIV. Dimensionality Reduction (Week 8)/14 - 6 - Reconstruction from Compressed Representation (4 min).srt 7.7 kB
VIII. Neural Networks Representation (Week 4)/8 - 7 - Multiclass Classification (4 min).srt 7.6 kB
III. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).txt 7.2 kB
XIII. Clustering (Week 8)/13 - 1 - Unsupervised Learning- Introduction (3 min).srt 7.2 kB
IV. Linear Regression with Multiple Variables (Week 2)/4 - 2 - Gradient Descent for Multiple Variables (5 min).srt 6.8 kB
V. Octave Tutorial (Week 2)/5 - 7 - Working on and Submitting Programming Exercises (4 min).srt 4.5 kB

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