[FreeCourseSite.com] Udemy - Machine Learning with Javascript

mp4   Hot：176   Size：10.74 GB   Created：2020-02-15 17:46:18   Update：2021-12-12 02:00:42  

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  • 5. Getting Started with Gradient Descent/9. Why a Learning Rate.mp4 187.28 MB
    6. Gradient Descent with Tensorflow/13. How it All Works Together!.mp4 143.82 MB
    2. Algorithm Overview/13. Investigating Optimal K Values.mp4 129.14 MB
    5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.mp4 126.77 MB
    5. Getting Started with Gradient Descent/12. Multiple Terms in Action.vtt 123.17 MB
    5. Getting Started with Gradient Descent/12. Multiple Terms in Action.mp4 123.16 MB
    7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.mp4 121.42 MB
    5. Getting Started with Gradient Descent/7. Gradient Descent in Action.mp4 115.36 MB
    3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.mp4 114.29 MB
    1. What is Machine Learning/3. A Complete Walkthrough.mp4 109.14 MB
    11. Multi-Value Classification/4. A Single Instance Approach.vtt 103.57 MB
    11. Multi-Value Classification/4. A Single Instance Approach.mp4 103.56 MB
    6. Gradient Descent with Tensorflow/8. Interpreting Results.mp4 101.71 MB
    13. Performance Optimization/6. Measuring Memory Usage.mp4 96.64 MB
    11. Multi-Value Classification/9. Marginal vs Conditional Probability.mp4 95.19 MB
    5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.mp4 93.47 MB
    2. Algorithm Overview/1. How K-Nearest Neighbor Works.mp4 93.33 MB
    4. Applications of Tensorflow/11. Normalization or Standardization.mp4 92.97 MB
    6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.mp4 90.8 MB
    4. Applications of Tensorflow/8. Loading CSV Data.mp4 89.33 MB
    12. Image Recognition In Action/8. Debugging the Calculation Process.mp4 89.05 MB
    6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.mp4 87.93 MB
    10. Natural Binary Classification/13. A Touch More Refactoring.mp4 87.43 MB
    4. Applications of Tensorflow/14. Debugging Calculations.mp4 86.72 MB
    7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.mp4 84.81 MB
    7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.mp4 82.36 MB
    7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.mp4 80.37 MB
    2. Algorithm Overview/22. Feature Selection with KNN.mp4 80.37 MB
    12. Image Recognition In Action/6. Implementing an Accuracy Gauge.mp4 79.95 MB
    9. Gradient Descent Alterations/6. Making Predictions with the Model.mp4 79.49 MB
    10. Natural Binary Classification/5. Decision Boundaries.mp4 79.18 MB
    2. Algorithm Overview/16. N-Dimension Distance.mp4 78.88 MB
    4. Applications of Tensorflow/3. KNN with Tensorflow.mp4 78.72 MB
    5. Getting Started with Gradient Descent/6. Derivatives!.mp4 77.96 MB
    5. Getting Started with Gradient Descent/6. Derivatives!.vtt 77.96 MB
    9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.mp4 77.24 MB
    7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.mp4 76.69 MB
    3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.mp4 76.62 MB
    7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.mp4 75.79 MB
    14. Appendix Custom CSV Loader/10. Splitting Test and Training.mp4 75.66 MB
    2. Algorithm Overview/19. Feature Normalization.mp4 72.92 MB
    7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.mp4 72.72 MB
    7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.mp4 71.42 MB
    2. Algorithm Overview/17. Arbitrary Feature Spaces.mp4 71.26 MB
    2. Algorithm Overview/14. Updating KNN for Multiple Features.mp4 70.62 MB
    10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.mp4 70.29 MB
    10. Natural Binary Classification/16. Variable Decision Boundaries.mp4 68.32 MB
    6. Gradient Descent with Tensorflow/9. Matrix Multiplication.mp4 67.47 MB
    9. Gradient Descent Alterations/4. Iterating Over Batches.mp4 67.46 MB
    6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.mp4 67.14 MB
    2. Algorithm Overview/20. Normalization with MinMax.mp4 67.05 MB
    9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.mp4 66.24 MB
    7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.mp4 66.16 MB
    11. Multi-Value Classification/8. Training a Multinominal Model.mp4 66.09 MB
    9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.mp4 66.09 MB
    2. Algorithm Overview/23. Objective Feature Picking.mp4 65.98 MB
    5. Getting Started with Gradient Descent/8. Quick Breather and Review.mp4 65.8 MB
    2. Algorithm Overview/2. Lodash Review.mp4 64.94 MB
    2. Algorithm Overview/2. Lodash Review.vtt 64.94 MB
    4. Applications of Tensorflow/10. Reporting Error Percentages.mp4 64.5 MB
    2. Algorithm Overview/18. Magnitude Offsets in Features.mp4 64.06 MB
    6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.mp4 63.25 MB
    4. Applications of Tensorflow/5. Sorting Tensors.mp4 62.85 MB
    1. What is Machine Learning/2. Solving Machine Learning Problems.mp4 62.78 MB
    11. Multi-Value Classification/10. Sigmoid vs Softmax.mp4 62.76 MB
    6. Gradient Descent with Tensorflow/3. Default Algorithm Options.mp4 62.66 MB
    7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.mp4 62.15 MB
    3. Onwards to Tensorflow JS!/6. Broadcasting Operations.mp4 62.06 MB
    12. Image Recognition In Action/5. Encoding Label Values.mp4 62.01 MB
    8. Plotting Data with Javascript/2. Plotting MSE Values.mp4 61.4 MB
    10. Natural Binary Classification/2. Logistic Regression in Action.mp4 61.07 MB
    10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.mp4 60.2 MB
    6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.mp4 59.6 MB
    10. Natural Binary Classification/7. Project Setup for Logistic Regression.mp4 59.41 MB
    2. Algorithm Overview/3. Implementing KNN.mp4 59.34 MB
    3. Onwards to Tensorflow JS!/10. Creating Slices of Data.mp4 58.92 MB
    3. Onwards to Tensorflow JS!/5. Elementwise Operations.mp4 58.36 MB
    4. Applications of Tensorflow/6. Averaging Top Values.mp4 58.13 MB
    7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.mp4 57.96 MB
    12. Image Recognition In Action/4. Flattening Image Data.mp4 57.77 MB
    4. Applications of Tensorflow/4. Maintaining Order Relationships.mp4 57.76 MB
    14. Appendix Custom CSV Loader/8. Extracting Data Columns.mp4 57.28 MB
    6. Gradient Descent with Tensorflow/1. Project Overview.mp4 57.04 MB
    3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.mp4 57.02 MB
    13. Performance Optimization/5. Shallow vs Retained Memory Usage.mp4 56.9 MB
    5. Getting Started with Gradient Descent/5. Observations Around MSE.mp4 56.11 MB
    13. Performance Optimization/4. The Javascript Garbage Collector.mp4 55.81 MB
    10. Natural Binary Classification/3. Bad Equation Fits.mp4 55.39 MB
    12. Image Recognition In Action/2. Greyscale Values.mp4 55.35 MB
    9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.mp4 55.11 MB
    13. Performance Optimization/21. Improving Model Accuracy.mp4 55.02 MB
    4. Applications of Tensorflow/1. KNN with Regression.mp4 54.99 MB
    10. Natural Binary Classification/15. Implementing a Test Function.mp4 54.71 MB
    2. Algorithm Overview/10. Gauging Accuracy.mp4 54.02 MB
    4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.mp4 53.06 MB
    4. Applications of Tensorflow/9. Running an Analysis.mp4 52.5 MB
    2. Algorithm Overview/12. Refactoring Accuracy Reporting.mp4 52.31 MB
    14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.mp4 52.14 MB
    7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.mp4 51.95 MB
    5. Getting Started with Gradient Descent/2. Why Linear Regression.mp4 50.35 MB
    2. Algorithm Overview/4. Finishing KNN Implementation.mp4 50.28 MB
    11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.mp4 49.97 MB
    10. Natural Binary Classification/18. Refactoring with Cross Entropy.mp4 49.46 MB
    10. Natural Binary Classification/19. Finishing the Cost Refactor.mp4 49.1 MB
    13. Performance Optimization/3. Creating Memory Snapshots.mp4 49.06 MB
    11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.mp4 48.88 MB
    3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.mp4 48.66 MB
    10. Natural Binary Classification/10. Encoding Label Values.mp4 48.59 MB
    11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.mp4 48.5 MB
    11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.mp4 48.46 MB
    1. What is Machine Learning/7. Dataset Structures.mp4 48.25 MB
    12. Image Recognition In Action/9. Dealing with Zero Variances.mp4 47.91 MB
    7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.mp4 47.85 MB
    8. Plotting Data with Javascript/3. Plotting MSE History against B Values.mp4 47.81 MB
    13. Performance Optimization/17. Plotting Cost History.mp4 47.6 MB
    1. What is Machine Learning/9. What Type of Problem.mp4 47.04 MB
    13. Performance Optimization/10. Tensorflow's Eager Memory Usage.mp4 46.81 MB
    13. Performance Optimization/19. Fixing Cost History.mp4 46.78 MB
    13. Performance Optimization/18. NaN in Cost History.mp4 46.37 MB
    6. Gradient Descent with Tensorflow/1. Project Overview.vtt 46.14 MB
    13. Performance Optimization/13. Tidying the Training Loop.mp4 45.99 MB
    8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.mp4 45.84 MB
    10. Natural Binary Classification/4. The Sigmoid Equation.mp4 45.45 MB
    2. Algorithm Overview/21. Applying Normalization.mp4 45.36 MB
    2. Algorithm Overview/7. Test and Training Data.mp4 45.21 MB
    2. Algorithm Overview/5. Testing the Algorithm.mp4 44.97 MB
    12. Image Recognition In Action/3. Many Features.mp4 44.77 MB
    11. Multi-Value Classification/7. Classifying Continuous Values.mp4 44.56 MB
    7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.mp4 44.5 MB
    13. Performance Optimization/1. Handing Large Datasets.mp4 44.47 MB
    2. Algorithm Overview/15. Multi-Dimensional KNN.vtt 44.22 MB
    2. Algorithm Overview/15. Multi-Dimensional KNN.mp4 44.21 MB
    5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.mp4 44.2 MB
    3. Onwards to Tensorflow JS!/11. Tensor Concatenation.mp4 44.14 MB
    6. Gradient Descent with Tensorflow/2. Data Loading.mp4 43.49 MB
    13. Performance Optimization/8. Measuring Footprint Reduction.mp4 43.31 MB
    10. Natural Binary Classification/20. Plotting Changing Cost History.mp4 42.95 MB
    4. Applications of Tensorflow/15. What Now.mp4 42.33 MB
    4. Applications of Tensorflow/13. Applying Standardization.mp4 41.47 MB
    4. Applications of Tensorflow/13. Applying Standardization.vtt 41.47 MB
    3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.mp4 41.37 MB
    4. Applications of Tensorflow/2. A Change in Data Structure.vtt 41.36 MB
    4. Applications of Tensorflow/2. A Change in Data Structure.mp4 41.35 MB
    5. Getting Started with Gradient Descent/10. Answering Common Questions.mp4 40.95 MB
    2. Algorithm Overview/6. Interpreting Bad Results.mp4 40.76 MB
    2. Algorithm Overview/9. Generalizing KNN.mp4 39 MB
    10. Natural Binary Classification/9. Importing Vehicle Data.mp4 38.96 MB
    11. Multi-Value Classification/3. A Smarter Refactor!.mp4 38.3 MB
    13. Performance Optimization/2. Minimizing Memory Usage.mp4 38.19 MB
    13. Performance Optimization/12. Implementing TF Tidy.mp4 37.6 MB
    7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.mp4 37.18 MB
    14. Appendix Custom CSV Loader/7. Custom Value Parsing.mp4 36.72 MB
    10. Natural Binary Classification/14. Gauging Classification Accuracy.mp4 36.71 MB
    7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.mp4 36.44 MB
    13. Performance Optimization/16. Final Memory Report.mp4 36.25 MB
    2. Algorithm Overview/8. Randomizing Test Data.mp4 36.01 MB
    13. Performance Optimization/7. Releasing References.mp4 35.98 MB
    4. Applications of Tensorflow/7. Moving to the Editor.mp4 34.33 MB
    1. What is Machine Learning/6. Identifying Relevant Data.mp4 33.91 MB
    6. Gradient Descent with Tensorflow/7. Updating Coefficients.vtt 33.88 MB
    6. Gradient Descent with Tensorflow/7. Updating Coefficients.mp4 33.86 MB
    7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.mp4 33.84 MB
    2. Algorithm Overview/11. Printing a Report.mp4 33.3 MB
    10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.mp4 32.78 MB
    1. What is Machine Learning/8. Recording Observation Data.mp4 32.74 MB
    14. Appendix Custom CSV Loader/6. Parsing Number Values.mp4 31.37 MB
    11. Multi-Value Classification/13. Calculating Accuracy.mp4 31.31 MB
    1. What is Machine Learning/5. Problem Outline.mp4 31.22 MB
    3. Onwards to Tensorflow JS!/9. Tensor Accessors.mp4 30.46 MB
    11. Multi-Value Classification/12. Implementing Accuracy Gauges.mp4 28.72 MB
    2. Algorithm Overview/24. Evaluating Different Feature Values.mp4 27.97 MB
    6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.mp4 27.68 MB
    13. Performance Optimization/15. One More Optimization.mp4 27.5 MB
    13. Performance Optimization/15. One More Optimization.vtt 27.5 MB
    3. Onwards to Tensorflow JS!/8. Logging Tensor Data.mp4 26 MB
    12. Image Recognition In Action/10. Backfilling Variance.mp4 25.73 MB
    12. Image Recognition In Action/10. Backfilling Variance.vtt 25.73 MB
    5. Getting Started with Gradient Descent/1. Linear Regression.vtt 25.39 MB
    5. Getting Started with Gradient Descent/1. Linear Regression.mp4 25.39 MB
    11. Multi-Value Classification/1. Multinominal Logistic Regression.mp4 25 MB
    12. Image Recognition In Action/1. Handwriting Recognition.mp4 24.7 MB
    13. Performance Optimization/11. Cleaning up Tensors with Tidy.mp4 24.27 MB
    10. Natural Binary Classification/1. Introducing Logistic Regression.mp4 23.45 MB
    13. Performance Optimization/20. Massaging Learning Parameters.mp4 22.56 MB
    14. Appendix Custom CSV Loader/4. Splitting into Columns.mp4 20.35 MB
    12. Image Recognition In Action/7. Unchanging Accuracy.mp4 20.3 MB
    1. What is Machine Learning/4. App Setup.mp4 19.27 MB
    14. Appendix Custom CSV Loader/3. Reading Files from Disk.mp4 18.6 MB
    13. Performance Optimization/9. Optimization Tensorflow Memory Usage.mp4 18.54 MB
    14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.mp4 18.41 MB
    13. Performance Optimization/14. Measuring Reduced Memory Usage.mp4 18.12 MB
    14. Appendix Custom CSV Loader/1. Loading CSV Files.mp4 15.86 MB
    10. Natural Binary Classification/6. Changes for Logistic Regression.mp4 12.5 MB
    14. Appendix Custom CSV Loader/2. A Test Dataset.mp4 9.59 MB
    1. What is Machine Learning/1. Getting Started - How to Get Help.mp4 8.36 MB
    10. Natural Binary Classification/8.1 regressions.zip.zip 34.3 KB
    5. Getting Started with Gradient Descent/9. Why a Learning Rate.vtt 22.69 KB
    6. Gradient Descent with Tensorflow/13. How it All Works Together!.vtt 18.25 KB
    5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.vtt 17 KB
    3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.vtt 16.68 KB
    5. Getting Started with Gradient Descent/7. Gradient Descent in Action.vtt 16.07 KB
    7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.vtt 15.95 KB
    2. Algorithm Overview/13. Investigating Optimal K Values.vtt 15.72 KB
    11. Multi-Value Classification/9. Marginal vs Conditional Probability.vtt 14.02 KB
    6. Gradient Descent with Tensorflow/8. Interpreting Results.vtt 13.6 KB
    5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.vtt 13.57 KB
    2. Algorithm Overview/16. N-Dimension Distance.vtt 13.39 KB
    1. What is Machine Learning/3. A Complete Walkthrough.vtt 13.34 KB
    4. Applications of Tensorflow/8. Loading CSV Data.vtt 13.19 KB
    4. Applications of Tensorflow/3. KNN with Tensorflow.vtt 12.97 KB
    6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.vtt 12.59 KB
    6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.vtt 12.48 KB
    7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.vtt 12.16 KB
    13. Performance Optimization/6. Measuring Memory Usage.vtt 12.01 KB
    2. Algorithm Overview/17. Arbitrary Feature Spaces.vtt 11.73 KB
    7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.vtt 11.67 KB
    4. Applications of Tensorflow/14. Debugging Calculations.vtt 11.46 KB
    2. Algorithm Overview/1. How K-Nearest Neighbor Works.vtt 11.46 KB
    12. Image Recognition In Action/8. Debugging the Calculation Process.vtt 11.31 KB
    6. Gradient Descent with Tensorflow/3. Default Algorithm Options.vtt 11.23 KB
    2. Algorithm Overview/22. Feature Selection with KNN.vtt 11.2 KB
    7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.vtt 11.06 KB
    3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.vtt 10.83 KB
    3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.vtt 10.78 KB
    4. Applications of Tensorflow/5. Sorting Tensors.vtt 10.64 KB
    10. Natural Binary Classification/5. Decision Boundaries.vtt 10.56 KB
    9. Gradient Descent Alterations/4. Iterating Over Batches.vtt 10.56 KB
    7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.vtt 10.49 KB
    9. Gradient Descent Alterations/6. Making Predictions with the Model.vtt 10.48 KB
    3. Onwards to Tensorflow JS!/5. Elementwise Operations.vtt 10.45 KB
    14. Appendix Custom CSV Loader/10. Splitting Test and Training.vtt 10.45 KB
    7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.vtt 10.41 KB
    10. Natural Binary Classification/13. A Touch More Refactoring.vtt 10.38 KB
    4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.vtt 10.34 KB
    7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.vtt 10.33 KB
    4. Applications of Tensorflow/11. Normalization or Standardization.vtt 10.3 KB
    4. Applications of Tensorflow/6. Averaging Top Values.vtt 10.28 KB
    7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.vtt 10.23 KB
    3. Onwards to Tensorflow JS!/10. Creating Slices of Data.vtt 10.17 KB
    2. Algorithm Overview/19. Feature Normalization.vtt 10.15 KB
    9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.vtt 10.11 KB
    10. Natural Binary Classification/16. Variable Decision Boundaries.vtt 10.06 KB
    12. Image Recognition In Action/6. Implementing an Accuracy Gauge.vtt 10.01 KB
    6. Gradient Descent with Tensorflow/9. Matrix Multiplication.vtt 9.91 KB
    10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.vtt 9.8 KB
    10. Natural Binary Classification/2. Logistic Regression in Action.vtt 9.49 KB
    3. Onwards to Tensorflow JS!/6. Broadcasting Operations.vtt 9.35 KB
    4. Applications of Tensorflow/4. Maintaining Order Relationships.vtt 9.3 KB
    2. Algorithm Overview/3. Implementing KNN.vtt 9.26 KB
    2. Algorithm Overview/20. Normalization with MinMax.vtt 9.1 KB
    2. Algorithm Overview/14. Updating KNN for Multiple Features.vtt 9.05 KB
    13. Performance Optimization/4. The Javascript Garbage Collector.vtt 8.95 KB
    7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.vtt 8.86 KB
    7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.vtt 8.86 KB
    12. Image Recognition In Action/9. Dealing with Zero Variances.vtt 8.78 KB
    11. Multi-Value Classification/10. Sigmoid vs Softmax.vtt 8.64 KB
    11. Multi-Value Classification/8. Training a Multinominal Model.vtt 8.62 KB
    6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.vtt 8.49 KB
    6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.vtt 8.46 KB
    6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.vtt 8.24 KB
    2. Algorithm Overview/23. Objective Feature Picking.vtt 8.23 KB
    4. Applications of Tensorflow/10. Reporting Error Percentages.vtt 8.22 KB
    1. What is Machine Learning/2. Solving Machine Learning Problems.vtt 8.19 KB
    5. Getting Started with Gradient Descent/5. Observations Around MSE.vtt 8.19 KB
    4. Applications of Tensorflow/9. Running an Analysis.vtt 8.16 KB
    10. Natural Binary Classification/7. Project Setup for Logistic Regression.vtt 8.14 KB
    1. What is Machine Learning/7. Dataset Structures.vtt 8.12 KB
    13. Performance Optimization/5. Shallow vs Retained Memory Usage.vtt 8.05 KB
    5. Getting Started with Gradient Descent/8. Quick Breather and Review.vtt 8.05 KB
    9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.vtt 7.98 KB
    7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.vtt 7.86 KB
    10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.vtt 7.86 KB
    9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.vtt 7.84 KB
    12. Image Recognition In Action/4. Flattening Image Data.vtt 7.8 KB
    2. Algorithm Overview/4. Finishing KNN Implementation.vtt 7.72 KB
    10. Natural Binary Classification/3. Bad Equation Fits.vtt 7.62 KB
    2. Algorithm Overview/18. Magnitude Offsets in Features.vtt 7.61 KB
    7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.vtt 7.52 KB
    3. Onwards to Tensorflow JS!/9. Tensor Accessors.vtt 7.48 KB
    10. Natural Binary Classification/15. Implementing a Test Function.vtt 7.46 KB
    3. Onwards to Tensorflow JS!/11. Tensor Concatenation.vtt 7.45 KB
    12. Image Recognition In Action/5. Encoding Label Values.vtt 7.42 KB
    14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.vtt 7.42 KB
    3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.vtt 7.27 KB
    11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.vtt 7.26 KB
    8. Plotting Data with Javascript/2. Plotting MSE Values.vtt 7.23 KB
    10. Natural Binary Classification/18. Refactoring with Cross Entropy.vtt 7.18 KB
    13. Performance Optimization/3. Creating Memory Snapshots.vtt 7.16 KB
    7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.vtt 7.11 KB
    9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.vtt 7.09 KB
    4. Applications of Tensorflow/1. KNN with Regression.vtt 7.06 KB
    12. Image Recognition In Action/2. Greyscale Values.vtt 7.02 KB
    2. Algorithm Overview/10. Gauging Accuracy.vtt 7 KB
    3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.vtt 6.85 KB
    6. Gradient Descent with Tensorflow/2. Data Loading.vtt 6.83 KB
    14. Appendix Custom CSV Loader/8. Extracting Data Columns.vtt 6.83 KB
    5. Getting Started with Gradient Descent/2. Why Linear Regression.vtt 6.76 KB
    1. What is Machine Learning/9. What Type of Problem.vtt 6.74 KB
    2. Algorithm Overview/12. Refactoring Accuracy Reporting.vtt 6.71 KB
    11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.vtt 6.69 KB
    13. Performance Optimization/2. Minimizing Memory Usage.vtt 6.6 KB
    5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.vtt 6.57 KB
    11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.vtt 6.5 KB
    10. Natural Binary Classification/4. The Sigmoid Equation.vtt 6.42 KB
    11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.vtt 6.33 KB
    13. Performance Optimization/19. Fixing Cost History.vtt 6.3 KB
    2. Algorithm Overview/5. Testing the Algorithm.vtt 6.24 KB
    8. Plotting Data with Javascript/3. Plotting MSE History against B Values.vtt 6.22 KB
    11. Multi-Value Classification/7. Classifying Continuous Values.vtt 6.18 KB
    13. Performance Optimization/1. Handing Large Datasets.vtt 6.17 KB
    7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.vtt 6.15 KB
    13. Performance Optimization/10. Tensorflow's Eager Memory Usage.vtt 6.14 KB
    2. Algorithm Overview/21. Applying Normalization.vtt 6.04 KB
    10. Natural Binary Classification/10. Encoding Label Values.vtt 6.03 KB
    13. Performance Optimization/18. NaN in Cost History.vtt 6.02 KB
    10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.vtt 5.99 KB
    10. Natural Binary Classification/19. Finishing the Cost Refactor.vtt 5.96 KB
    8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.vtt 5.93 KB
    13. Performance Optimization/21. Improving Model Accuracy.vtt 5.93 KB
    1. What is Machine Learning/6. Identifying Relevant Data.vtt 5.88 KB
    10. Natural Binary Classification/9. Importing Vehicle Data.vtt 5.85 KB
    2. Algorithm Overview/6. Interpreting Bad Results.vtt 5.78 KB
    13. Performance Optimization/17. Plotting Cost History.vtt 5.77 KB
    14. Appendix Custom CSV Loader/7. Custom Value Parsing.vtt 5.75 KB
    4. Applications of Tensorflow/15. What Now.vtt 5.68 KB
    3. Onwards to Tensorflow JS!/8. Logging Tensor Data.vtt 5.5 KB
    13. Performance Optimization/13. Tidying the Training Loop.vtt 5.48 KB
    13. Performance Optimization/8. Measuring Footprint Reduction.vtt 5.46 KB
    2. Algorithm Overview/7. Test and Training Data.vtt 5.38 KB
    1. What is Machine Learning/8. Recording Observation Data.vtt 5.34 KB
    5. Getting Started with Gradient Descent/10. Answering Common Questions.vtt 5.25 KB
    11. Multi-Value Classification/3. A Smarter Refactor!.vtt 5.24 KB
    2. Algorithm Overview/8. Randomizing Test Data.vtt 5.01 KB
    2. Algorithm Overview/9. Generalizing KNN.vtt 4.98 KB
    10. Natural Binary Classification/20. Plotting Changing Cost History.vtt 4.95 KB
    7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.vtt 4.91 KB
    10. Natural Binary Classification/14. Gauging Classification Accuracy.vtt 4.81 KB
    7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.vtt 4.8 KB
    14. Appendix Custom CSV Loader/6. Parsing Number Values.vtt 4.79 KB
    13. Performance Optimization/12. Implementing TF Tidy.vtt 4.77 KB
    12. Image Recognition In Action/3. Many Features.vtt 4.74 KB
    4. Applications of Tensorflow/7. Moving to the Editor.vtt 4.66 KB
    11. Multi-Value Classification/13. Calculating Accuracy.vtt 4.46 KB
    6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.vtt 4.44 KB
    2. Algorithm Overview/11. Printing a Report.vtt 4.4 KB
    13. Performance Optimization/7. Releasing References.vtt 4.36 KB
    1. What is Machine Learning/5. Problem Outline.vtt 4.31 KB
    7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.vtt 4.16 KB
    13. Performance Optimization/16. Final Memory Report.vtt 3.93 KB
    14. Appendix Custom CSV Loader/3. Reading Files from Disk.vtt 3.91 KB
    13. Performance Optimization/11. Cleaning up Tensors with Tidy.vtt 3.86 KB
    11. Multi-Value Classification/12. Implementing Accuracy Gauges.vtt 3.76 KB
    14. Appendix Custom CSV Loader/4. Splitting into Columns.vtt 3.74 KB
    2. Algorithm Overview/24. Evaluating Different Feature Values.vtt 3.71 KB
    10. Natural Binary Classification/1. Introducing Logistic Regression.vtt 3.45 KB
    14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.vtt 3.42 KB
    11. Multi-Value Classification/1. Multinominal Logistic Regression.vtt 3.16 KB
    12. Image Recognition In Action/1. Handwriting Recognition.vtt 3.15 KB
    1. What is Machine Learning/4. App Setup.vtt 3.03 KB
    14. Appendix Custom CSV Loader/1. Loading CSV Files.vtt 2.98 KB
    12. Image Recognition In Action/7. Unchanging Accuracy.vtt 2.89 KB
    14. Appendix Custom CSV Loader/2. A Test Dataset.vtt 2.55 KB
    13. Performance Optimization/20. Massaging Learning Parameters.vtt 2.44 KB
    13. Performance Optimization/9. Optimization Tensorflow Memory Usage.vtt 2.35 KB
    13. Performance Optimization/14. Measuring Reduced Memory Usage.vtt 2.19 KB
    10. Natural Binary Classification/6. Changes for Logistic Regression.vtt 1.76 KB
    1. What is Machine Learning/1. Getting Started - How to Get Help.vtt 1.55 KB
    10. Natural Binary Classification/8. Project Download.html 215 B
    3. Onwards to Tensorflow JS!/4. Tensor Dimension and Shapes.html 139 B
    3. Onwards to Tensorflow JS!/7. Broadcasting Elementwise Operations.html 139 B
    [FCS Forum].url 133 B
    [FreeCourseSite.com].url 127 B
    [CourseClub.NET].url 123 B

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