Machine Learning

mp4   Hot：236   Size：1.39 GB   Created：2017-09-13 16:21:38   Update：2021-09-12 12:29:58  

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    Lectures/1 - 1 - Welcome (7 min).mp4 11.95 MB
    Lectures/1 - 2 - What is Machine Learning- (7 min).mp4 9.35 MB
    Lectures/1 - 3 - Supervised Learning (12 min).mp4 13.45 MB
    Lectures/1 - 4 - Unsupervised Learning (14 min).mp4 16.66 MB
    Lectures/10 - 1 - Deciding What to Try Next (6 min).mp4 6.86 MB
    Lectures/10 - 2 - Evaluating a Hypothesis (8 min).mp4 8.48 MB
    Lectures/10 - 3 - Model Selection and Train-Validation-Test Sets (12 min).mp4 14.07 MB
    Lectures/10 - 4 - Diagnosing Bias vs. Variance (8 min).mp4 8.97 MB
    Lectures/10 - 5 - Regularization and Bias-Variance (11 min).mp4 12.6 MB
    Lectures/10 - 6 - Learning Curves (12 min).mp4 12.92 MB
    Lectures/10 - 7 - Deciding What to Do Next Revisited (7 min).mp4 8.18 MB
    Lectures/11 - 1 - Prioritizing What to Work On (10 min).mp4 11.17 MB
    Lectures/11 - 2 - Error Analysis (13 min).mp4 15.43 MB
    Lectures/11 - 3 - Error Metrics for Skewed Classes (12 min).mp4 13.25 MB
    Lectures/11 - 4 - Trading Off Precision and Recall (14 min).mp4 15.99 MB
    Lectures/11 - 5 - Data For Machine Learning (11 min).mp4 12.87 MB
    Lectures/12 - 1 - Optimization Objective (15 min).mp4 16.65 MB
    Lectures/12 - 2 - Large Margin Intuition (11 min).mp4 11.81 MB
    Lectures/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4 21.83 MB
    Lectures/12 - 4 - Kernels I (16 min).mp4 17.57 MB
    Lectures/12 - 5 - Kernels II (16 min).mp4 17.45 MB
    Lectures/12 - 6 - Using An SVM (21 min).mp4 23.95 MB
    Lectures/13 - 1 - Unsupervised Learning- Introduction (3 min).mp4 3.8 MB
    Lectures/13 - 2 - K-Means Algorithm (13 min).mp4 13.81 MB
    Lectures/13 - 3 - Optimization Objective (7 min).mp4 8.15 MB
    Lectures/13 - 4 - Random Initialization (8 min).mp4 8.67 MB
    Lectures/13 - 5 - Choosing the Number of Clusters (8 min).mp4 9.4 MB
    Lectures/14 - 1 - Motivation I- Data Compression (10 min).mp4 14.31 MB
    Lectures/14 - 2 - Motivation II- Visualization (6 min).mp4 6.3 MB
    Lectures/14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp4 10.45 MB
    Lectures/14 - 4 - Principal Component Analysis Algorithm (15 min).mp4 17.79 MB
    Lectures/14 - 5 - Choosing the Number of Principal Components (11 min).mp4 11.84 MB
    Lectures/14 - 6 - Reconstruction from Compressed Representation (4 min).mp4 4.98 MB
    Lectures/14 - 7 - Advice for Applying PCA (13 min).mp4 14.7 MB
    Lectures/15 - 1 - Problem Motivation (8 min).mp4 8.35 MB
    Lectures/15 - 2 - Gaussian Distribution (10 min).mp4 11.69 MB
    Lectures/15 - 3 - Algorithm (12 min).mp4 13.95 MB
    Lectures/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4 15.15 MB
    Lectures/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp4 9.28 MB
    Lectures/15 - 6 - Choosing What Features to Use (12 min).mp4 14.12 MB
    Lectures/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4 15.93 MB
    Lectures/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4 16.34 MB
    Lectures/16 - 2 - Content Based Recommendations (15 min).mp4 16.93 MB
    Lectures/16 - 3 - Collaborative Filtering (10 min).mp4 11.75 MB
    Lectures/16 - 4 - Collaborative Filtering Algorithm (9 min).mp4 10.31 MB
    Lectures/16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).mp4 9.68 MB
    Lectures/16 - 6 - Implementational Detail- Mean Normalization (9 min).mp4 9.71 MB
    Lectures/17 - 1 - Learning With Large Datasets (6 min).mp4 6.5 MB
    Lectures/17 - 2 - Stochastic Gradient Descent (13 min).mp4 15.33 MB
    Lectures/17 - 3 - Mini-Batch Gradient Descent (6 min).mp4 7.32 MB
    Lectures/17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4 13.33 MB
    Lectures/17 - 5 - Online Learning (13 min).mp4 14.91 MB
    Lectures/17 - 6 - Map Reduce and Data Parallelism (14 min).mp4 16.06 MB
    Lectures/18 - 1 - Problem Description and Pipeline (7 min).mp4 7.91 MB
    Lectures/18 - 2 - Sliding Windows (15 min).mp4 16.52 MB
    Lectures/18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4 18.82 MB
    Lectures/18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).mp4 16.11 MB
    Lectures/19 - 1 - Summary and Thank You (5 min).mp4 6.09 MB
    Lectures/2 - 1 - Model Representation (8 min).mp4 9 MB
    Lectures/2 - 2 - Cost Function (8 min).mp4 9.05 MB
    Lectures/2 - 3 - Cost Function - Intuition I (11 min).mp4 12.24 MB
    Lectures/2 - 4 - Cost Function - Intuition II (9 min).mp4 11.36 MB
    Lectures/2 - 5 - Gradient Descent (11 min).mp4 13.5 MB
    Lectures/2 - 6 - Gradient Descent Intuition (12 min).mp4 13.03 MB
    Lectures/2 - 7 - Gradient Descent For Linear Regression (10 min).mp4 12.18 MB
    Lectures/2 - 8 - What-'s Next (6 min).mp4 6.08 MB
    Lectures/3 - 1 - Matrices and Vectors (9 min).mp4 9.56 MB
    Lectures/3 - 2 - Addition and Scalar Multiplication (7 min).mp4 7.46 MB
    Lectures/3 - 3 - Matrix Vector Multiplication (14 min).mp4 15 MB
    Lectures/3 - 4 - Matrix Matrix Multiplication (11 min).mp4 12.59 MB
    Lectures/3 - 5 - Matrix Multiplication Properties (9 min).mp4 9.81 MB
    Lectures/3 - 6 - Inverse and Transpose (11 min).mp4 12.87 MB
    Lectures/4 - 1 - Multiple Features (8 min).mp4 8.84 MB
    Lectures/4 - 2 - Gradient Descent for Multiple Variables (5 min).mp4 5.78 MB
    Lectures/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp4 9.46 MB
    Lectures/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp4 9.26 MB
    Lectures/4 - 5 - Features and Polynomial Regression (8 min).mp4 8.26 MB
    Lectures/4 - 6 - Normal Equation (16 min).mp4 17.13 MB
    Lectures/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp4 6.24 MB
    Lectures/5 - 1 - Basic Operations (14 min).mp4 17.72 MB
    Lectures/5 - 2 - Moving Data Around (16 min).mp4 20.77 MB
    Lectures/5 - 3 - Computing on Data (13 min).mp4 15.25 MB
    Lectures/5 - 4 - Plotting Data (10 min).mp4 13.32 MB
    Lectures/5 - 5 - Control Statements- for, while, if statements (13 min).mp4 16.49 MB
    Lectures/5 - 6 - Vectorization (14 min).mp4 16.09 MB
    Lectures/5 - 7 - Working on and Submitting Programming Exercises (4 min).mp4 5.46 MB
    Lectures/6 - 1 - Classification (8 min).mp4 8.77 MB
    Lectures/6 - 2 - Hypothesis Representation (7 min).mp4 8.34 MB
    Lectures/6 - 3 - Decision Boundary (15 min).mp4 16.74 MB
    Lectures/6 - 4 - Cost Function (11 min).mp4 13.09 MB
    Lectures/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp4 11.96 MB
    Lectures/6 - 6 - Advanced Optimization (14 min).mp4 18.15 MB
    Lectures/6 - 7 - Multiclass Classification- One-vs-all (6 min).mp4 6.93 MB
    Lectures/7 - 1 - The Problem of Overfitting (10 min).mp4 11.15 MB
    Lectures/7 - 2 - Cost Function (10 min).mp4 11.63 MB
    Lectures/7 - 3 - Regularized Linear Regression (11 min) (1).mp4 12 MB
    Lectures/7 - 3 - Regularized Linear Regression (11 min).mp4 12 MB
    Lectures/7 - 4 - Regularized Logistic Regression (9 min).mp4 10.89 MB
    Lectures/8 - 1 - Non-linear Hypotheses (10 min).mp4 10.88 MB
    Lectures/8 - 2 - Neurons and the Brain (8 min).mp4 9.89 MB
    Lectures/8 - 3 - Model Representation I (12 min).mp4 13.51 MB
    Lectures/8 - 4 - Model Representation II (12 min).mp4 13.45 MB
    Lectures/8 - 5 - Examples and Intuitions I (7 min).mp4 7.89 MB
    Lectures/8 - 6 - Examples and Intuitions II (10 min).mp4 14 MB
    Lectures/8 - 7 - Multiclass Classification (4 min).mp4 4.83 MB
    Lectures/9 - 1 - Cost Function (7 min).mp4 7.66 MB
    Lectures/9 - 2 - Backpropagation Algorithm (12 min).mp4 13.94 MB
    Lectures/9 - 3 - Backpropagation Intuition (13 min).mp4 15.44 MB
    Lectures/9 - 4 - Implementation Note- Unrolling Parameters (8 min).mp4 9.38 MB
    Lectures/9 - 5 - Gradient Checking (12 min).mp4 13.5 MB
    Lectures/9 - 7 - Putting It Together (14 min).mp4 16.3 MB
    Lectures/9 - 8 - Autonomous Driving (7 min).mp4 14.88 MB

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