[FreeCourseSite.com] Udemy - Machine Learning with Javascript

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[FreeCourseSite.com] Udemy - Machine Learning with Javascript

磁力链接/BT种子简介

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收录时间：2021-04-21
最近下载：2025-06-24

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文件列表

  • 5. Getting Started with Gradient Descent/9. Why a Learning Rate.mp4 196.4 MB
  • 6. Gradient Descent with Tensorflow/13. How it All Works Together!.mp4 150.8 MB
  • 2. Algorithm Overview/13. Investigating Optimal K Values.mp4 135.4 MB
  • 5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.mp4 132.9 MB
  • 5. Getting Started with Gradient Descent/12. Multiple Terms in Action.mp4 129.1 MB
  • 7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.mp4 127.3 MB
  • 5. Getting Started with Gradient Descent/7. Gradient Descent in Action.mp4 121.0 MB
  • 3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.mp4 119.8 MB
  • 1. What is Machine Learning/3. A Complete Walkthrough.mp4 114.4 MB
  • 2. Algorithm Overview/12. Refactoring Accuracy Reporting.srt 109.7 MB
  • 11. Multi-Value Classification/4. A Single Instance Approach.mp4 108.6 MB
  • 6. Gradient Descent with Tensorflow/8. Interpreting Results.mp4 106.7 MB
  • 13. Performance Optimization/6. Measuring Memory Usage.mp4 101.3 MB
  • 11. Multi-Value Classification/9. Marginal vs Conditional Probability.mp4 99.8 MB
  • 5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.mp4 98.0 MB
  • 2. Algorithm Overview/1. How K-Nearest Neighbor Works.mp4 97.9 MB
  • 4. Applications of Tensorflow/11. Normalization or Standardization.mp4 97.5 MB
  • 6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.mp4 95.2 MB
  • 4. Applications of Tensorflow/8. Loading CSV Data.mp4 93.7 MB
  • 12. Image Recognition In Action/8. Debugging the Calculation Process.mp4 93.4 MB
  • 6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.mp4 92.2 MB
  • 10. Natural Binary Classification/13. A Touch More Refactoring.mp4 91.7 MB
  • 4. Applications of Tensorflow/14. Debugging Calculations.mp4 90.9 MB
  • 7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.mp4 88.9 MB
  • 7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.mp4 86.4 MB
  • 7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.mp4 84.3 MB
  • 2. Algorithm Overview/22. Feature Selection with KNN.mp4 84.3 MB
  • 12. Image Recognition In Action/6. Implementing an Accuracy Gauge.mp4 83.8 MB
  • 9. Gradient Descent Alterations/6. Making Predictions with the Model.srt 83.4 MB
  • 9. Gradient Descent Alterations/6. Making Predictions with the Model.mp4 83.3 MB
  • 10. Natural Binary Classification/5. Decision Boundaries.mp4 83.0 MB
  • 2. Algorithm Overview/16. N-Dimension Distance.mp4 82.7 MB
  • 4. Applications of Tensorflow/3. KNN with Tensorflow.mp4 82.5 MB
  • 5. Getting Started with Gradient Descent/6. Derivatives!.mp4 81.7 MB
  • 9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.mp4 81.0 MB
  • 7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.mp4 80.4 MB
  • 3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.mp4 80.3 MB
  • 7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.mp4 79.5 MB
  • 14. Appendix Custom CSV Loader/10. Splitting Test and Training.mp4 79.3 MB
  • 2. Algorithm Overview/19. Feature Normalization.srt 76.5 MB
  • 2. Algorithm Overview/19. Feature Normalization.mp4 76.4 MB
  • 7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.mp4 76.2 MB
  • 7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.mp4 74.9 MB
  • 2. Algorithm Overview/17. Arbitrary Feature Spaces.mp4 74.7 MB
  • 2. Algorithm Overview/14. Updating KNN for Multiple Features.mp4 74.0 MB
  • 10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.mp4 73.7 MB
  • 10. Natural Binary Classification/16. Variable Decision Boundaries.mp4 71.6 MB
  • 6. Gradient Descent with Tensorflow/9. Matrix Multiplication.srt 70.8 MB
  • 6. Gradient Descent with Tensorflow/9. Matrix Multiplication.mp4 70.7 MB
  • 9. Gradient Descent Alterations/4. Iterating Over Batches.mp4 70.7 MB
  • 6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.mp4 70.4 MB
  • 2. Algorithm Overview/20. Normalization with MinMax.mp4 70.3 MB
  • 9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.mp4 69.5 MB
  • 7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.mp4 69.4 MB
  • 11. Multi-Value Classification/8. Training a Multinominal Model.mp4 69.3 MB
  • 9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.mp4 69.3 MB
  • 2. Algorithm Overview/23. Objective Feature Picking.mp4 69.2 MB
  • 5. Getting Started with Gradient Descent/8. Quick Breather and Review.mp4 69.0 MB
  • 2. Algorithm Overview/2. Lodash Review.mp4 68.1 MB
  • 4. Applications of Tensorflow/10. Reporting Error Percentages.mp4 67.6 MB
  • 2. Algorithm Overview/18. Magnitude Offsets in Features.mp4 67.2 MB
  • 6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.mp4 66.3 MB
  • 4. Applications of Tensorflow/5. Sorting Tensors.mp4 65.9 MB
  • 1. What is Machine Learning/2. Solving Machine Learning Problems.mp4 65.8 MB
  • 11. Multi-Value Classification/10. Sigmoid vs Softmax.mp4 65.8 MB
  • 6. Gradient Descent with Tensorflow/3. Default Algorithm Options.mp4 65.7 MB
  • 7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.mp4 65.2 MB
  • 3. Onwards to Tensorflow JS!/6. Broadcasting Operations.mp4 65.1 MB
  • 12. Image Recognition In Action/5. Encoding Label Values.mp4 65.0 MB
  • 8. Plotting Data with Javascript/2. Plotting MSE Values.mp4 64.4 MB
  • 10. Natural Binary Classification/2. Logistic Regression in Action.mp4 64.0 MB
  • 10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.mp4 63.1 MB
  • 6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.mp4 62.5 MB
  • 10. Natural Binary Classification/7. Project Setup for Logistic Regression.mp4 62.3 MB
  • 2. Algorithm Overview/3. Implementing KNN.mp4 62.2 MB
  • 3. Onwards to Tensorflow JS!/10. Creating Slices of Data.mp4 61.8 MB
  • 3. Onwards to Tensorflow JS!/5. Elementwise Operations.mp4 61.2 MB
  • 4. Applications of Tensorflow/6. Averaging Top Values.mp4 61.0 MB
  • 7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.mp4 60.8 MB
  • 12. Image Recognition In Action/4. Flattening Image Data.mp4 60.6 MB
  • 4. Applications of Tensorflow/4. Maintaining Order Relationships.mp4 60.6 MB
  • 14. Appendix Custom CSV Loader/8. Extracting Data Columns.mp4 60.0 MB
  • 6. Gradient Descent with Tensorflow/1. Project Overview.mp4 59.8 MB
  • 3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.mp4 59.8 MB
  • 13. Performance Optimization/5. Shallow vs Retained Memory Usage.mp4 59.7 MB
  • 5. Getting Started with Gradient Descent/5. Observations Around MSE.mp4 58.8 MB
  • 13. Performance Optimization/4. The Javascript Garbage Collector.mp4 58.5 MB
  • 10. Natural Binary Classification/3. Bad Equation Fits.mp4 58.1 MB
  • 12. Image Recognition In Action/2. Greyscale Values.mp4 58.0 MB
  • 9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.mp4 57.8 MB
  • 13. Performance Optimization/21. Improving Model Accuracy.mp4 57.7 MB
  • 4. Applications of Tensorflow/1. KNN with Regression.mp4 57.6 MB
  • 10. Natural Binary Classification/15. Implementing a Test Function.mp4 57.4 MB
  • 2. Algorithm Overview/10. Gauging Accuracy.mp4 56.6 MB
  • 4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.mp4 55.6 MB
  • 4. Applications of Tensorflow/9. Running an Analysis.mp4 55.1 MB
  • 2. Algorithm Overview/12. Refactoring Accuracy Reporting.mp4 54.8 MB
  • 14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.srt 54.7 MB
  • 14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.mp4 54.7 MB
  • 7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.mp4 54.5 MB
  • 5. Getting Started with Gradient Descent/2. Why Linear Regression.mp4 52.8 MB
  • 2. Algorithm Overview/4. Finishing KNN Implementation.mp4 52.7 MB
  • 11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.mp4 52.4 MB
  • 10. Natural Binary Classification/18. Refactoring with Cross Entropy.mp4 51.8 MB
  • 10. Natural Binary Classification/19. Finishing the Cost Refactor.mp4 51.5 MB
  • 13. Performance Optimization/3. Creating Memory Snapshots.mp4 51.4 MB
  • 11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.mp4 51.2 MB
  • 3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.mp4 51.0 MB
  • 10. Natural Binary Classification/10. Encoding Label Values.mp4 50.9 MB
  • 11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.mp4 50.8 MB
  • 11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.mp4 50.8 MB
  • 1. What is Machine Learning/7. Dataset Structures.mp4 50.6 MB
  • 12. Image Recognition In Action/9. Dealing with Zero Variances.mp4 50.2 MB
  • 7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.mp4 50.2 MB
  • 8. Plotting Data with Javascript/3. Plotting MSE History against B Values.mp4 50.1 MB
  • 13. Performance Optimization/17. Plotting Cost History.mp4 49.9 MB
  • 1. What is Machine Learning/9. What Type of Problem.mp4 49.3 MB
  • 13. Performance Optimization/10. Tensorflow's Eager Memory Usage.mp4 49.1 MB
  • 13. Performance Optimization/19. Fixing Cost History.mp4 49.0 MB
  • 13. Performance Optimization/18. NaN in Cost History.mp4 48.6 MB
  • 13. Performance Optimization/13. Tidying the Training Loop.mp4 48.2 MB
  • 8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.mp4 48.1 MB
  • 10. Natural Binary Classification/4. The Sigmoid Equation.mp4 47.7 MB
  • 2. Algorithm Overview/21. Applying Normalization.mp4 47.6 MB
  • 2. Algorithm Overview/7. Test and Training Data.mp4 47.4 MB
  • 2. Algorithm Overview/5. Testing the Algorithm.mp4 47.2 MB
  • 12. Image Recognition In Action/3. Many Features.mp4 46.9 MB
  • 11. Multi-Value Classification/7. Classifying Continuous Values.mp4 46.7 MB
  • 7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.mp4 46.6 MB
  • 13. Performance Optimization/1. Handing Large Datasets.mp4 46.6 MB
  • 2. Algorithm Overview/15. Multi-Dimensional KNN.mp4 46.4 MB
  • 5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.mp4 46.4 MB
  • 3. Onwards to Tensorflow JS!/11. Tensor Concatenation.mp4 46.3 MB
  • 6. Gradient Descent with Tensorflow/2. Data Loading.srt 45.6 MB
  • 6. Gradient Descent with Tensorflow/2. Data Loading.mp4 45.6 MB
  • 13. Performance Optimization/8. Measuring Footprint Reduction.mp4 45.4 MB
  • 10. Natural Binary Classification/20. Plotting Changing Cost History.mp4 45.0 MB
  • 4. Applications of Tensorflow/15. What Now.mp4 44.4 MB
  • 4. Applications of Tensorflow/13. Applying Standardization.mp4 43.5 MB
  • 3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.mp4 43.4 MB
  • 4. Applications of Tensorflow/2. A Change in Data Structure.srt 43.4 MB
  • 4. Applications of Tensorflow/2. A Change in Data Structure.mp4 43.4 MB
  • 5. Getting Started with Gradient Descent/10. Answering Common Questions.mp4 42.9 MB
  • 2. Algorithm Overview/6. Interpreting Bad Results.mp4 42.7 MB
  • 2. Algorithm Overview/9. Generalizing KNN.mp4 40.9 MB
  • 10. Natural Binary Classification/9. Importing Vehicle Data.mp4 40.8 MB
  • 11. Multi-Value Classification/3. A Smarter Refactor!.mp4 40.2 MB
  • 13. Performance Optimization/2. Minimizing Memory Usage.mp4 40.0 MB
  • 13. Performance Optimization/12. Implementing TF Tidy.mp4 39.4 MB
  • 7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.mp4 39.0 MB
  • 14. Appendix Custom CSV Loader/7. Custom Value Parsing.mp4 38.5 MB
  • 10. Natural Binary Classification/14. Gauging Classification Accuracy.mp4 38.5 MB
  • 7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.mp4 38.2 MB
  • 13. Performance Optimization/16. Final Memory Report.mp4 38.0 MB
  • 2. Algorithm Overview/8. Randomizing Test Data.mp4 37.7 MB
  • 13. Performance Optimization/7. Releasing References.mp4 37.7 MB
  • 4. Applications of Tensorflow/7. Moving to the Editor.srt 36.0 MB
  • 4. Applications of Tensorflow/7. Moving to the Editor.mp4 36.0 MB
  • 1. What is Machine Learning/6. Identifying Relevant Data.mp4 35.6 MB
  • 6. Gradient Descent with Tensorflow/7. Updating Coefficients.mp4 35.5 MB
  • 7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.mp4 35.5 MB
  • 2. Algorithm Overview/11. Printing a Report.mp4 34.9 MB
  • 10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.mp4 34.4 MB
  • 1. What is Machine Learning/8. Recording Observation Data.mp4 34.3 MB
  • 14. Appendix Custom CSV Loader/6. Parsing Number Values.mp4 32.9 MB
  • 11. Multi-Value Classification/13. Calculating Accuracy.mp4 32.8 MB
  • 1. What is Machine Learning/5. Problem Outline.mp4 32.7 MB
  • 3. Onwards to Tensorflow JS!/9. Tensor Accessors.mp4 31.9 MB
  • 11. Multi-Value Classification/12. Implementing Accuracy Gauges.mp4 30.1 MB
  • 2. Algorithm Overview/24. Evaluating Different Feature Values.mp4 29.3 MB
  • 4. Applications of Tensorflow/6. Averaging Top Values.srt 29.1 MB
  • 6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.srt 29.0 MB
  • 6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.mp4 29.0 MB
  • 13. Performance Optimization/15. One More Optimization.srt 28.8 MB
  • 13. Performance Optimization/15. One More Optimization.mp4 28.8 MB
  • 3. Onwards to Tensorflow JS!/8. Logging Tensor Data.mp4 27.3 MB
  • 12. Image Recognition In Action/10. Backfilling Variance.mp4 27.0 MB
  • 5. Getting Started with Gradient Descent/1. Linear Regression.mp4 26.6 MB
  • 11. Multi-Value Classification/1. Multinominal Logistic Regression.mp4 26.2 MB
  • 12. Image Recognition In Action/1. Handwriting Recognition.mp4 25.9 MB
  • 13. Performance Optimization/11. Cleaning up Tensors with Tidy.mp4 25.4 MB
  • 10. Natural Binary Classification/1. Introducing Logistic Regression.mp4 24.6 MB
  • 13. Performance Optimization/20. Massaging Learning Parameters.mp4 23.6 MB
  • 14. Appendix Custom CSV Loader/4. Splitting into Columns.mp4 21.3 MB
  • 12. Image Recognition In Action/7. Unchanging Accuracy.mp4 21.3 MB
  • 1. What is Machine Learning/4. App Setup.mp4 20.2 MB
  • 14. Appendix Custom CSV Loader/3. Reading Files from Disk.mp4 19.5 MB
  • 13. Performance Optimization/9. Optimization Tensorflow Memory Usage.mp4 19.4 MB
  • 14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.mp4 19.3 MB
  • 13. Performance Optimization/14. Measuring Reduced Memory Usage.mp4 19.0 MB
  • 14. Appendix Custom CSV Loader/1. Loading CSV Files.mp4 16.6 MB
  • 14. Appendix Custom CSV Loader/4. Splitting into Columns.srt 14.7 MB
  • 10. Natural Binary Classification/6. Changes for Logistic Regression.mp4 13.1 MB
  • 14. Appendix Custom CSV Loader/2. A Test Dataset.mp4 10.0 MB
  • 1. What is Machine Learning/1. Getting Started - How to Get Help.mp4 8.8 MB
  • 3. Onwards to Tensorflow JS!/6. Broadcasting Operations.srt 2.1 MB
  • 10. Natural Binary Classification/8.1 regressions.zip.zip 35.1 kB
  • 5. Getting Started with Gradient Descent/9. Why a Learning Rate.srt 26.6 kB
  • 6. Gradient Descent with Tensorflow/13. How it All Works Together!.srt 21.4 kB
  • 5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.srt 19.9 kB
  • 3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.srt 19.5 kB
  • 5. Getting Started with Gradient Descent/7. Gradient Descent in Action.srt 18.9 kB
  • 7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.srt 18.6 kB
  • 2. Algorithm Overview/13. Investigating Optimal K Values.srt 18.5 kB
  • 5. Getting Started with Gradient Descent/12. Multiple Terms in Action.srt 16.9 kB
  • 11. Multi-Value Classification/9. Marginal vs Conditional Probability.srt 16.4 kB
  • 6. Gradient Descent with Tensorflow/8. Interpreting Results.srt 15.8 kB
  • 5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.srt 15.8 kB
  • 11. Multi-Value Classification/4. A Single Instance Approach.srt 15.7 kB
  • 2. Algorithm Overview/16. N-Dimension Distance.srt 15.6 kB
  • 2. Algorithm Overview/2. Lodash Review.srt 15.6 kB
  • 1. What is Machine Learning/3. A Complete Walkthrough.srt 15.5 kB
  • 4. Applications of Tensorflow/8. Loading CSV Data.srt 15.4 kB
  • 4. Applications of Tensorflow/3. KNN with Tensorflow.srt 15.3 kB
  • 6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.srt 14.8 kB
  • 6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.srt 14.6 kB
  • 7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.srt 14.2 kB
  • 13. Performance Optimization/6. Measuring Memory Usage.srt 14.2 kB
  • 2. Algorithm Overview/17. Arbitrary Feature Spaces.srt 13.7 kB
  • 7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.srt 13.6 kB
  • 4. Applications of Tensorflow/14. Debugging Calculations.srt 13.3 kB
  • 2. Algorithm Overview/1. How K-Nearest Neighbor Works.srt 13.3 kB
  • 12. Image Recognition In Action/8. Debugging the Calculation Process.srt 13.3 kB
  • 2. Algorithm Overview/22. Feature Selection with KNN.srt 13.0 kB
  • 6. Gradient Descent with Tensorflow/3. Default Algorithm Options.srt 13.0 kB
  • 7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.srt 12.8 kB
  • 3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.srt 12.6 kB
  • 3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.srt 12.6 kB
  • 9. Gradient Descent Alterations/4. Iterating Over Batches.srt 12.4 kB
  • 4. Applications of Tensorflow/5. Sorting Tensors.srt 12.4 kB
  • 14. Appendix Custom CSV Loader/10. Splitting Test and Training.srt 12.3 kB
  • 7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.srt 12.2 kB
  • 10. Natural Binary Classification/5. Decision Boundaries.srt 12.2 kB
  • 3. Onwards to Tensorflow JS!/5. Elementwise Operations.srt 12.2 kB
  • 7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.srt 12.2 kB
  • 4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.srt 12.1 kB
  • 10. Natural Binary Classification/13. A Touch More Refactoring.srt 12.1 kB
  • 4. Applications of Tensorflow/11. Normalization or Standardization.srt 12.0 kB
  • 7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.srt 12.0 kB
  • 7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.srt 11.9 kB
  • 3. Onwards to Tensorflow JS!/10. Creating Slices of Data.srt 11.9 kB
  • 9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.srt 11.7 kB
  • 12. Image Recognition In Action/6. Implementing an Accuracy Gauge.srt 11.7 kB
  • 10. Natural Binary Classification/16. Variable Decision Boundaries.srt 11.7 kB
  • 10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.srt 11.4 kB
  • 5. Getting Started with Gradient Descent/6. Derivatives!.srt 11.2 kB
  • 10. Natural Binary Classification/2. Logistic Regression in Action.srt 11.1 kB
  • 4. Applications of Tensorflow/4. Maintaining Order Relationships.srt 10.9 kB
  • 2. Algorithm Overview/3. Implementing KNN.srt 10.8 kB
  • 2. Algorithm Overview/20. Normalization with MinMax.srt 10.6 kB
  • 2. Algorithm Overview/14. Updating KNN for Multiple Features.srt 10.5 kB
  • 13. Performance Optimization/4. The Javascript Garbage Collector.srt 10.4 kB
  • 7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.srt 10.3 kB
  • 7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.srt 10.3 kB
  • 12. Image Recognition In Action/9. Dealing with Zero Variances.srt 10.2 kB
  • 11. Multi-Value Classification/8. Training a Multinominal Model.srt 10.1 kB
  • 11. Multi-Value Classification/10. Sigmoid vs Softmax.srt 10.1 kB
  • 6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.srt 9.9 kB
  • 6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.srt 9.8 kB
  • 6. Gradient Descent with Tensorflow/1. Project Overview.srt 9.7 kB
  • 6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.srt 9.7 kB
  • 2. Algorithm Overview/23. Objective Feature Picking.srt 9.6 kB
  • 4. Applications of Tensorflow/9. Running an Analysis.srt 9.6 kB
  • 4. Applications of Tensorflow/10. Reporting Error Percentages.srt 9.5 kB
  • 5. Getting Started with Gradient Descent/5. Observations Around MSE.srt 9.5 kB
  • 1. What is Machine Learning/2. Solving Machine Learning Problems.srt 9.5 kB
  • 10. Natural Binary Classification/7. Project Setup for Logistic Regression.srt 9.5 kB
  • 1. What is Machine Learning/7. Dataset Structures.srt 9.4 kB
  • 5. Getting Started with Gradient Descent/8. Quick Breather and Review.srt 9.4 kB
  • 13. Performance Optimization/5. Shallow vs Retained Memory Usage.srt 9.3 kB
  • 9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.srt 9.3 kB
  • 7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.srt 9.2 kB
  • 10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.srt 9.1 kB
  • 12. Image Recognition In Action/4. Flattening Image Data.srt 9.0 kB
  • 2. Algorithm Overview/4. Finishing KNN Implementation.srt 9.0 kB
  • 9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.srt 9.0 kB
  • 2. Algorithm Overview/18. Magnitude Offsets in Features.srt 8.9 kB
  • 10. Natural Binary Classification/3. Bad Equation Fits.srt 8.8 kB
  • 10. Natural Binary Classification/15. Implementing a Test Function.srt 8.8 kB
  • 3. Onwards to Tensorflow JS!/9. Tensor Accessors.srt 8.8 kB
  • 3. Onwards to Tensorflow JS!/11. Tensor Concatenation.srt 8.7 kB
  • 7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.srt 8.7 kB
  • 12. Image Recognition In Action/5. Encoding Label Values.srt 8.7 kB
  • 3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.srt 8.5 kB
  • 11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.srt 8.4 kB
  • 8. Plotting Data with Javascript/2. Plotting MSE Values.srt 8.4 kB
  • 10. Natural Binary Classification/18. Refactoring with Cross Entropy.srt 8.4 kB
  • 13. Performance Optimization/3. Creating Memory Snapshots.srt 8.4 kB
  • 7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.srt 8.3 kB
  • 9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.srt 8.2 kB
  • 4. Applications of Tensorflow/1. KNN with Regression.srt 8.2 kB
  • 2. Algorithm Overview/10. Gauging Accuracy.srt 8.2 kB
  • 12. Image Recognition In Action/2. Greyscale Values.srt 8.1 kB
  • 14. Appendix Custom CSV Loader/8. Extracting Data Columns.srt 7.9 kB
  • 3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.srt 7.9 kB
  • 11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.srt 7.8 kB
  • 5. Getting Started with Gradient Descent/2. Why Linear Regression.srt 7.8 kB
  • 1. What is Machine Learning/9. What Type of Problem.srt 7.8 kB
  • 11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.srt 7.7 kB
  • 13. Performance Optimization/2. Minimizing Memory Usage.srt 7.7 kB
  • 5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.srt 7.6 kB
  • 10. Natural Binary Classification/4. The Sigmoid Equation.srt 7.4 kB
  • 11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.srt 7.3 kB
  • 13. Performance Optimization/19. Fixing Cost History.srt 7.3 kB
  • 2. Algorithm Overview/5. Testing the Algorithm.srt 7.3 kB
  • 8. Plotting Data with Javascript/3. Plotting MSE History against B Values.srt 7.2 kB
  • 13. Performance Optimization/1. Handing Large Datasets.srt 7.2 kB
  • 11. Multi-Value Classification/7. Classifying Continuous Values.srt 7.2 kB
  • 7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.srt 7.1 kB
  • 13. Performance Optimization/10. Tensorflow's Eager Memory Usage.srt 7.1 kB
  • 2. Algorithm Overview/21. Applying Normalization.srt 7.1 kB
  • 13. Performance Optimization/18. NaN in Cost History.srt 7.1 kB
  • 10. Natural Binary Classification/10. Encoding Label Values.srt 7.0 kB
  • 10. Natural Binary Classification/19. Finishing the Cost Refactor.srt 7.0 kB
  • 8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.srt 7.0 kB
  • 10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.srt 6.9 kB
  • 13. Performance Optimization/21. Improving Model Accuracy.srt 6.9 kB
  • 1. What is Machine Learning/6. Identifying Relevant Data.srt 6.8 kB
  • 10. Natural Binary Classification/9. Importing Vehicle Data.srt 6.8 kB
  • 13. Performance Optimization/17. Plotting Cost History.srt 6.8 kB
  • 14. Appendix Custom CSV Loader/7. Custom Value Parsing.srt 6.7 kB
  • 2. Algorithm Overview/6. Interpreting Bad Results.srt 6.6 kB
  • 4. Applications of Tensorflow/15. What Now.srt 6.5 kB
  • 2. Algorithm Overview/15. Multi-Dimensional KNN.srt 6.5 kB
  • 13. Performance Optimization/13. Tidying the Training Loop.srt 6.4 kB
  • 3. Onwards to Tensorflow JS!/8. Logging Tensor Data.srt 6.4 kB
  • 13. Performance Optimization/8. Measuring Footprint Reduction.srt 6.4 kB
  • 4. Applications of Tensorflow/13. Applying Standardization.srt 6.3 kB
  • 2. Algorithm Overview/7. Test and Training Data.srt 6.2 kB
  • 1. What is Machine Learning/8. Recording Observation Data.srt 6.2 kB
  • 5. Getting Started with Gradient Descent/10. Answering Common Questions.srt 6.1 kB
  • 11. Multi-Value Classification/3. A Smarter Refactor!.srt 6.1 kB
  • 10. Natural Binary Classification/20. Plotting Changing Cost History.srt 5.8 kB
  • 2. Algorithm Overview/8. Randomizing Test Data.srt 5.8 kB
  • 2. Algorithm Overview/9. Generalizing KNN.srt 5.8 kB
  • 7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.srt 5.7 kB
  • 14. Appendix Custom CSV Loader/6. Parsing Number Values.srt 5.6 kB
  • 7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.srt 5.6 kB
  • 10. Natural Binary Classification/14. Gauging Classification Accuracy.srt 5.6 kB
  • 13. Performance Optimization/12. Implementing TF Tidy.srt 5.5 kB
  • 12. Image Recognition In Action/3. Many Features.srt 5.5 kB
  • 11. Multi-Value Classification/13. Calculating Accuracy.srt 5.2 kB
  • 2. Algorithm Overview/11. Printing a Report.srt 5.1 kB
  • 6. Gradient Descent with Tensorflow/7. Updating Coefficients.srt 5.1 kB
  • 13. Performance Optimization/7. Releasing References.srt 5.1 kB
  • 1. What is Machine Learning/5. Problem Outline.srt 5.0 kB
  • 7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.srt 4.8 kB
  • 5. Getting Started with Gradient Descent/1. Linear Regression.srt 4.6 kB
  • 13. Performance Optimization/16. Final Memory Report.srt 4.6 kB
  • 14. Appendix Custom CSV Loader/3. Reading Files from Disk.srt 4.5 kB
  • 13. Performance Optimization/11. Cleaning up Tensors with Tidy.srt 4.5 kB
  • 11. Multi-Value Classification/12. Implementing Accuracy Gauges.srt 4.4 kB
  • 2. Algorithm Overview/24. Evaluating Different Feature Values.srt 4.3 kB
  • 12. Image Recognition In Action/10. Backfilling Variance.srt 4.2 kB
  • 10. Natural Binary Classification/1. Introducing Logistic Regression.srt 4.0 kB
  • 14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.srt 4.0 kB
  • 11. Multi-Value Classification/1. Multinominal Logistic Regression.srt 3.7 kB
  • 12. Image Recognition In Action/1. Handwriting Recognition.srt 3.7 kB
  • 1. What is Machine Learning/4. App Setup.srt 3.5 kB
  • 14. Appendix Custom CSV Loader/1. Loading CSV Files.srt 3.5 kB
  • 12. Image Recognition In Action/7. Unchanging Accuracy.srt 3.3 kB
  • 14. Appendix Custom CSV Loader/2. A Test Dataset.srt 3.0 kB
  • 13. Performance Optimization/20. Massaging Learning Parameters.srt 2.8 kB
  • 13. Performance Optimization/9. Optimization Tensorflow Memory Usage.srt 2.7 kB
  • 13. Performance Optimization/14. Measuring Reduced Memory Usage.srt 2.5 kB
  • 15. Extras/1. Bonus!.html 2.4 kB
  • 10. Natural Binary Classification/6. Changes for Logistic Regression.srt 2.0 kB
  • 1. What is Machine Learning/1. Getting Started - How to Get Help.srt 1.8 kB
  • 10. Natural Binary Classification/8. Project Download.html 215 Bytes
  • 3. Onwards to Tensorflow JS!/4. Tensor Dimension and Shapes.html 143 Bytes
  • 3. Onwards to Tensorflow JS!/7. Broadcasting Elementwise Operations.html 143 Bytes
  • 0. Websites you may like/[FCS Forum].url 133 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 0. Websites you may like/[CourseClub.ME].url 122 Bytes

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