[FreeCourseLab.com] Udemy - Machine Learning with Javascript

Creation Time	March 22, 2025, 11:50 a.m.
Last Access Time	Oct. 15, 2025, 2:03 a.m.
File Size	10.2 GB
Keywords	with Javascript - Udemy Machine Learning FreeCourseLab com
Total Requests	12
Total Files	371
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

5. Getting Started with Gradient Descent/9. Why a Learning Rate.mp4 187.3 MB
6. Gradient Descent with Tensorflow/13. How it All Works Together!.mp4 143.8 MB
2. Algorithm Overview/13. Investigating Optimal K Values.mp4 129.1 MB
5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.mp4 126.8 MB
5. Getting Started with Gradient Descent/12. Multiple Terms in Action.mp4 123.2 MB
7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.mp4 121.4 MB
5. Getting Started with Gradient Descent/7. Gradient Descent in Action.mp4 115.4 MB
3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.mp4 114.3 MB
1. What is Machine Learning/3. A Complete Walkthrough.mp4 109.1 MB
11. Multi-Value Classification/4. A Single Instance Approach.mp4 103.6 MB
6. Gradient Descent with Tensorflow/8. Interpreting Results.mp4 101.7 MB
13. Performance Optimization/6. Measuring Memory Usage.mp4 96.6 MB
11. Multi-Value Classification/9. Marginal vs Conditional Probability.mp4 95.2 MB
5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.mp4 93.5 MB
2. Algorithm Overview/1. How K-Nearest Neighbor Works.mp4 93.3 MB
4. Applications of Tensorflow/11. Normalization or Standardization.mp4 93.0 MB
6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.mp4 90.8 MB
4. Applications of Tensorflow/8. Loading CSV Data.mp4 89.3 MB
12. Image Recognition In Action/8. Debugging the Calculation Process.mp4 89.1 MB
6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.mp4 87.9 MB
10. Natural Binary Classification/13. A Touch More Refactoring.mp4 87.4 MB
4. Applications of Tensorflow/14. Debugging Calculations.mp4 86.7 MB
7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.mp4 84.8 MB
7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.mp4 82.4 MB
7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.mp4 80.4 MB
2. Algorithm Overview/22. Feature Selection with KNN.mp4 80.4 MB
12. Image Recognition In Action/6. Implementing an Accuracy Gauge.mp4 79.9 MB
9. Gradient Descent Alterations/6. Making Predictions with the Model.mp4 79.5 MB
10. Natural Binary Classification/5. Decision Boundaries.mp4 79.2 MB
2. Algorithm Overview/16. N-Dimension Distance.mp4 78.9 MB
4. Applications of Tensorflow/3. KNN with Tensorflow.mp4 78.7 MB
5. Getting Started with Gradient Descent/6. Derivatives!.mp4 77.9 MB
9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.mp4 77.2 MB
7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.mp4 76.7 MB
3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.mp4 76.6 MB
7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.mp4 75.8 MB
14. Appendix Custom CSV Loader/10. Splitting Test and Training.mp4 75.6 MB
2. Algorithm Overview/19. Feature Normalization.mp4 72.9 MB
7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.mp4 72.7 MB
7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.mp4 71.4 MB
2. Algorithm Overview/17. Arbitrary Feature Spaces.mp4 71.2 MB
2. Algorithm Overview/14. Updating KNN for Multiple Features.mp4 70.6 MB
10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.mp4 70.3 MB
10. Natural Binary Classification/16. Variable Decision Boundaries.mp4 68.3 MB
6. Gradient Descent with Tensorflow/9. Matrix Multiplication.mp4 67.5 MB
9. Gradient Descent Alterations/4. Iterating Over Batches.mp4 67.5 MB
6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.mp4 67.1 MB
2. Algorithm Overview/20. Normalization with MinMax.mp4 67.0 MB
9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.mp4 66.2 MB
7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.mp4 66.2 MB
11. Multi-Value Classification/8. Training a Multinominal Model.mp4 66.1 MB
9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.mp4 66.1 MB
2. Algorithm Overview/23. Objective Feature Picking.mp4 66.0 MB
5. Getting Started with Gradient Descent/8. Quick Breather and Review.mp4 65.8 MB
2. Algorithm Overview/2. Lodash Review.mp4 64.9 MB
4. Applications of Tensorflow/10. Reporting Error Percentages.mp4 64.5 MB
2. Algorithm Overview/18. Magnitude Offsets in Features.mp4 64.1 MB
6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.mp4 63.2 MB
4. Applications of Tensorflow/5. Sorting Tensors.mp4 62.8 MB
1. What is Machine Learning/2. Solving Machine Learning Problems.mp4 62.8 MB
11. Multi-Value Classification/10. Sigmoid vs Softmax.mp4 62.8 MB
6. Gradient Descent with Tensorflow/3. Default Algorithm Options.vtt 62.7 MB
6. Gradient Descent with Tensorflow/3. Default Algorithm Options.mp4 62.7 MB
7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.mp4 62.2 MB
3. Onwards to Tensorflow JS!/6. Broadcasting Operations.mp4 62.1 MB
12. Image Recognition In Action/5. Encoding Label Values.mp4 62.0 MB
8. Plotting Data with Javascript/2. Plotting MSE Values.mp4 61.4 MB
10. Natural Binary Classification/2. Logistic Regression in Action.mp4 61.1 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 59.4 MB
2. Algorithm Overview/3. Implementing KNN.mp4 59.3 MB
3. Onwards to Tensorflow JS!/10. Creating Slices of Data.mp4 58.9 MB
3. Onwards to Tensorflow JS!/5. Elementwise Operations.mp4 58.4 MB
4. Applications of Tensorflow/6. Averaging Top Values.mp4 58.1 MB
7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.mp4 58.0 MB
12. Image Recognition In Action/4. Flattening Image Data.mp4 57.8 MB
4. Applications of Tensorflow/4. Maintaining Order Relationships.mp4 57.8 MB
14. Appendix Custom CSV Loader/8. Extracting Data Columns.mp4 57.3 MB
6. Gradient Descent with Tensorflow/1. Project Overview.mp4 57.0 MB
3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.mp4 57.0 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 56.1 MB
13. Performance Optimization/4. The Javascript Garbage Collector.mp4 55.8 MB
10. Natural Binary Classification/3. Bad Equation Fits.mp4 55.4 MB
12. Image Recognition In Action/2. Greyscale Values.mp4 55.4 MB
9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.mp4 55.1 MB
13. Performance Optimization/21. Improving Model Accuracy.mp4 55.0 MB
4. Applications of Tensorflow/1. KNN with Regression.mp4 55.0 MB
10. Natural Binary Classification/15. Implementing a Test Function.mp4 54.7 MB
2. Algorithm Overview/10. Gauging Accuracy.mp4 54.0 MB
4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.mp4 53.1 MB
4. Applications of Tensorflow/9. Running an Analysis.mp4 52.5 MB
2. Algorithm Overview/12. Refactoring Accuracy Reporting.mp4 52.3 MB
14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.mp4 52.1 MB
7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.mp4 51.9 MB
5. Getting Started with Gradient Descent/2. Why Linear Regression.mp4 50.3 MB
2. Algorithm Overview/4. Finishing KNN Implementation.mp4 50.3 MB
11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.mp4 50.0 MB
10. Natural Binary Classification/18. Refactoring with Cross Entropy.mp4 49.4 MB
10. Natural Binary Classification/19. Finishing the Cost Refactor.mp4 49.1 MB
13. Performance Optimization/3. Creating Memory Snapshots.mp4 49.1 MB
11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.mp4 48.9 MB
3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.mp4 48.6 MB
10. Natural Binary Classification/10. Encoding Label Values.mp4 48.6 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 48.5 MB
1. What is Machine Learning/7. Dataset Structures.mp4 48.2 MB
12. Image Recognition In Action/9. Dealing with Zero Variances.mp4 47.9 MB
7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.mp4 47.8 MB
8. Plotting Data with Javascript/3. Plotting MSE History against B Values.mp4 47.8 MB
13. Performance Optimization/17. Plotting Cost History.mp4 47.6 MB
1. What is Machine Learning/9. What Type of Problem.mp4 47.0 MB
13. Performance Optimization/10. Tensorflow's Eager Memory Usage.mp4 46.8 MB
13. Performance Optimization/19. Fixing Cost History.mp4 46.8 MB
13. Performance Optimization/18. NaN in Cost History.mp4 46.4 MB
13. Performance Optimization/13. Tidying the Training Loop.mp4 46.0 MB
8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.mp4 45.8 MB
10. Natural Binary Classification/4. The Sigmoid Equation.mp4 45.4 MB
2. Algorithm Overview/21. Applying Normalization.mp4 45.3 MB
2. Algorithm Overview/7. Test and Training Data.mp4 45.2 MB
2. Algorithm Overview/5. Testing the Algorithm.mp4 45.0 MB
12. Image Recognition In Action/3. Many Features.mp4 44.8 MB
11. Multi-Value Classification/7. Classifying Continuous Values.mp4 44.6 MB
7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.mp4 44.5 MB
13. Performance Optimization/1. Handing Large Datasets.mp4 44.5 MB
5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.mp4 44.2 MB
2. Algorithm Overview/15. Multi-Dimensional KNN.mp4 44.2 MB
3. Onwards to Tensorflow JS!/11. Tensor Concatenation.mp4 44.1 MB
6. Gradient Descent with Tensorflow/2. Data Loading.mp4 43.5 MB
13. Performance Optimization/8. Measuring Footprint Reduction.mp4 43.3 MB
10. Natural Binary Classification/20. Plotting Changing Cost History.mp4 43.0 MB
4. Applications of Tensorflow/15. What Now.mp4 42.3 MB
3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.vtt 42.0 MB
4. Applications of Tensorflow/13. Applying Standardization.mp4 41.5 MB
3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.mp4 41.4 MB
4. Applications of Tensorflow/2. A Change in Data Structure.mp4 41.4 MB
5. Getting Started with Gradient Descent/10. Answering Common Questions.mp4 40.9 MB
2. Algorithm Overview/6. Interpreting Bad Results.mp4 40.7 MB
2. Algorithm Overview/9. Generalizing KNN.mp4 39.0 MB
10. Natural Binary Classification/9. Importing Vehicle Data.mp4 39.0 MB
11. Multi-Value Classification/3. A Smarter Refactor!.mp4 38.3 MB
13. Performance Optimization/2. Minimizing Memory Usage.mp4 38.2 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 37.2 MB
14. Appendix Custom CSV Loader/7. Custom Value Parsing.mp4 36.7 MB
10. Natural Binary Classification/14. Gauging Classification Accuracy.mp4 36.7 MB
7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.mp4 36.5 MB
13. Performance Optimization/16. Final Memory Report.mp4 36.3 MB
2. Algorithm Overview/8. Randomizing Test Data.mp4 36.0 MB
13. Performance Optimization/7. Releasing References.mp4 36.0 MB
4. Applications of Tensorflow/7. Moving to the Editor.mp4 34.3 MB
1. What is Machine Learning/6. Identifying Relevant Data.mp4 33.9 MB
6. Gradient Descent with Tensorflow/7. Updating Coefficients.mp4 33.9 MB
7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.mp4 33.8 MB
2. Algorithm Overview/11. Printing a Report.mp4 33.3 MB
10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.mp4 32.8 MB
1. What is Machine Learning/8. Recording Observation Data.mp4 32.7 MB
14. Appendix Custom CSV Loader/6. Parsing Number Values.mp4 31.4 MB
11. Multi-Value Classification/13. Calculating Accuracy.mp4 31.3 MB
1. What is Machine Learning/5. Problem Outline.mp4 31.2 MB
3. Onwards to Tensorflow JS!/9. Tensor Accessors.mp4 30.5 MB
11. Multi-Value Classification/12. Implementing Accuracy Gauges.mp4 28.7 MB
2. Algorithm Overview/24. Evaluating Different Feature Values.mp4 28.0 MB
6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.mp4 27.7 MB
13. Performance Optimization/15. One More Optimization.mp4 27.5 MB
3. Onwards to Tensorflow JS!/8. Logging Tensor Data.mp4 26.0 MB
12. Image Recognition In Action/10. Backfilling Variance.mp4 25.7 MB
5. Getting Started with Gradient Descent/1. Linear Regression.mp4 25.4 MB
11. Multi-Value Classification/1. Multinominal Logistic Regression.mp4 25.0 MB
12. Image Recognition In Action/1. Handwriting Recognition.mp4 24.7 MB
13. Performance Optimization/11. Cleaning up Tensors with Tidy.mp4 24.3 MB
10. Natural Binary Classification/1. Introducing Logistic Regression.mp4 23.4 MB
13. Performance Optimization/20. Massaging Learning Parameters.mp4 22.6 MB
14. Appendix Custom CSV Loader/4. Splitting into Columns.mp4 20.3 MB
12. Image Recognition In Action/7. Unchanging Accuracy.mp4 20.3 MB
1. What is Machine Learning/4. App Setup.mp4 19.3 MB
14. Appendix Custom CSV Loader/3. Reading Files from Disk.mp4 18.6 MB
13. Performance Optimization/9. Optimization Tensorflow Memory Usage.mp4 18.5 MB
14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.mp4 18.4 MB
13. Performance Optimization/14. Measuring Reduced Memory Usage.mp4 18.1 MB
14. Appendix Custom CSV Loader/1. Loading CSV Files.mp4 15.9 MB
10. Natural Binary Classification/6. Changes for Logistic Regression.mp4 12.5 MB
14. Appendix Custom CSV Loader/2. A Test Dataset.mp4 9.6 MB
1. What is Machine Learning/1. Getting Started - How to Get Help.mp4 8.3 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 22.7 KB
6. Gradient Descent with Tensorflow/13. How it All Works Together!.vtt 18.2 KB
5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.vtt 17.0 KB
3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.vtt 16.7 KB
5. Getting Started with Gradient Descent/7. Gradient Descent in Action.vtt 16.1 KB
7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.vtt 15.9 KB
2. Algorithm Overview/13. Investigating Optimal K Values.vtt 15.7 KB
5. Getting Started with Gradient Descent/12. Multiple Terms in Action.vtt 14.4 KB
11. Multi-Value Classification/9. Marginal vs Conditional Probability.vtt 14.0 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 13.6 KB
11. Multi-Value Classification/4. A Single Instance Approach.vtt 13.5 KB
2. Algorithm Overview/2. Lodash Review.vtt 13.4 KB
2. Algorithm Overview/16. N-Dimension Distance.vtt 13.4 KB
1. What is Machine Learning/3. A Complete Walkthrough.vtt 13.3 KB
4. Applications of Tensorflow/8. Loading CSV Data.vtt 13.2 KB
4. Applications of Tensorflow/3. KNN with Tensorflow.vtt 13.0 KB
6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.vtt 12.6 KB
6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.vtt 12.5 KB
7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.vtt 12.2 KB
13. Performance Optimization/6. Measuring Memory Usage.vtt 12.0 KB
2. Algorithm Overview/17. Arbitrary Feature Spaces.vtt 11.7 KB
7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.vtt 11.7 KB
4. Applications of Tensorflow/14. Debugging Calculations.vtt 11.5 KB
2. Algorithm Overview/1. How K-Nearest Neighbor Works.vtt 11.5 KB
12. Image Recognition In Action/8. Debugging the Calculation Process.vtt 11.3 KB
2. Algorithm Overview/22. Feature Selection with KNN.vtt 11.2 KB
7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.vtt 11.1 KB
3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.vtt 10.8 KB
3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.vtt 10.8 KB
4. Applications of Tensorflow/5. Sorting Tensors.vtt 10.6 KB
10. Natural Binary Classification/5. Decision Boundaries.vtt 10.6 KB
9. Gradient Descent Alterations/4. Iterating Over Batches.vtt 10.6 KB
7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.vtt 10.5 KB
9. Gradient Descent Alterations/6. Making Predictions with the Model.vtt 10.5 KB
3. Onwards to Tensorflow JS!/5. Elementwise Operations.vtt 10.4 KB
14. Appendix Custom CSV Loader/10. Splitting Test and Training.vtt 10.4 KB
7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.vtt 10.4 KB
10. Natural Binary Classification/13. A Touch More Refactoring.vtt 10.4 KB
4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.vtt 10.3 KB
7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.vtt 10.3 KB
4. Applications of Tensorflow/11. Normalization or Standardization.vtt 10.3 KB
4. Applications of Tensorflow/6. Averaging Top Values.vtt 10.3 KB
7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.vtt 10.2 KB
3. Onwards to Tensorflow JS!/10. Creating Slices of Data.vtt 10.2 KB
2. Algorithm Overview/19. Feature Normalization.vtt 10.2 KB
9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.vtt 10.1 KB
10. Natural Binary Classification/16. Variable Decision Boundaries.vtt 10.1 KB
12. Image Recognition In Action/6. Implementing an Accuracy Gauge.vtt 10.0 KB
6. Gradient Descent with Tensorflow/9. Matrix Multiplication.vtt 9.9 KB
10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.vtt 9.8 KB
5. Getting Started with Gradient Descent/6. Derivatives!.vtt 9.6 KB
10. Natural Binary Classification/2. Logistic Regression in Action.vtt 9.5 KB
3. Onwards to Tensorflow JS!/6. Broadcasting Operations.vtt 9.4 KB
4. Applications of Tensorflow/4. Maintaining Order Relationships.vtt 9.3 KB
2. Algorithm Overview/3. Implementing KNN.vtt 9.3 KB
2. Algorithm Overview/20. Normalization with MinMax.vtt 9.1 KB
2. Algorithm Overview/14. Updating KNN for Multiple Features.vtt 9.0 KB
13. Performance Optimization/4. The Javascript Garbage Collector.vtt 9.0 KB
7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.vtt 8.9 KB
7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.vtt 8.9 KB
12. Image Recognition In Action/9. Dealing with Zero Variances.vtt 8.8 KB
11. Multi-Value Classification/10. Sigmoid vs Softmax.vtt 8.6 KB
11. Multi-Value Classification/8. Training a Multinominal Model.vtt 8.6 KB
6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.vtt 8.5 KB
6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.vtt 8.5 KB
6. Gradient Descent with Tensorflow/1. Project Overview.vtt 8.4 KB
6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.vtt 8.2 KB
2. Algorithm Overview/23. Objective Feature Picking.vtt 8.2 KB
4. Applications of Tensorflow/10. Reporting Error Percentages.vtt 8.2 KB
1. What is Machine Learning/2. Solving Machine Learning Problems.vtt 8.2 KB
5. Getting Started with Gradient Descent/5. Observations Around MSE.vtt 8.2 KB
4. Applications of Tensorflow/9. Running an Analysis.vtt 8.2 KB
10. Natural Binary Classification/7. Project Setup for Logistic Regression.vtt 8.1 KB
1. What is Machine Learning/7. Dataset Structures.vtt 8.1 KB
13. Performance Optimization/5. Shallow vs Retained Memory Usage.vtt 8.1 KB
5. Getting Started with Gradient Descent/8. Quick Breather and Review.vtt 8.0 KB
9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.vtt 8.0 KB
7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.vtt 7.9 KB
10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.vtt 7.9 KB
9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.vtt 7.8 KB
12. Image Recognition In Action/4. Flattening Image Data.vtt 7.8 KB
2. Algorithm Overview/4. Finishing KNN Implementation.vtt 7.7 KB
10. Natural Binary Classification/3. Bad Equation Fits.vtt 7.6 KB
2. Algorithm Overview/18. Magnitude Offsets in Features.vtt 7.6 KB
7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.vtt 7.5 KB
3. Onwards to Tensorflow JS!/9. Tensor Accessors.vtt 7.5 KB
10. Natural Binary Classification/15. Implementing a Test Function.vtt 7.5 KB
3. Onwards to Tensorflow JS!/11. Tensor Concatenation.vtt 7.5 KB
12. Image Recognition In Action/5. Encoding Label Values.vtt 7.4 KB
14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.vtt 7.4 KB
3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.vtt 7.3 KB
11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.vtt 7.3 KB
8. Plotting Data with Javascript/2. Plotting MSE Values.vtt 7.2 KB
10. Natural Binary Classification/18. Refactoring with Cross Entropy.vtt 7.2 KB
13. Performance Optimization/3. Creating Memory Snapshots.vtt 7.2 KB
7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.vtt 7.1 KB
9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.vtt 7.1 KB
4. Applications of Tensorflow/1. KNN with Regression.vtt 7.1 KB
12. Image Recognition In Action/2. Greyscale Values.vtt 7.0 KB
2. Algorithm Overview/10. Gauging Accuracy.vtt 7.0 KB
6. Gradient Descent with Tensorflow/2. Data Loading.vtt 6.8 KB
14. Appendix Custom CSV Loader/8. Extracting Data Columns.vtt 6.8 KB
5. Getting Started with Gradient Descent/2. Why Linear Regression.vtt 6.8 KB
1. What is Machine Learning/9. What Type of Problem.vtt 6.7 KB
2. Algorithm Overview/12. Refactoring Accuracy Reporting.vtt 6.7 KB
11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.vtt 6.7 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 6.6 KB
11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.vtt 6.5 KB
10. Natural Binary Classification/4. The Sigmoid Equation.vtt 6.4 KB
11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.vtt 6.3 KB
13. Performance Optimization/19. Fixing Cost History.vtt 6.3 KB
2. Algorithm Overview/5. Testing the Algorithm.vtt 6.2 KB
8. Plotting Data with Javascript/3. Plotting MSE History against B Values.vtt 6.2 KB
11. Multi-Value Classification/7. Classifying Continuous Values.vtt 6.2 KB
13. Performance Optimization/1. Handing Large Datasets.vtt 6.2 KB
7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.vtt 6.1 KB
13. Performance Optimization/10. Tensorflow's Eager Memory Usage.vtt 6.1 KB
2. Algorithm Overview/21. Applying Normalization.vtt 6.0 KB
10. Natural Binary Classification/10. Encoding Label Values.vtt 6.0 KB
13. Performance Optimization/18. NaN in Cost History.vtt 6.0 KB
10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.vtt 6.0 KB
10. Natural Binary Classification/19. Finishing the Cost Refactor.vtt 6.0 KB
8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.vtt 5.9 KB
13. Performance Optimization/21. Improving Model Accuracy.vtt 5.9 KB
1. What is Machine Learning/6. Identifying Relevant Data.vtt 5.9 KB
10. Natural Binary Classification/9. Importing Vehicle Data.vtt 5.9 KB
2. Algorithm Overview/6. Interpreting Bad Results.vtt 5.8 KB
13. Performance Optimization/17. Plotting Cost History.vtt 5.8 KB
4. Applications of Tensorflow/2. A Change in Data Structure.vtt 5.8 KB
14. Appendix Custom CSV Loader/7. Custom Value Parsing.vtt 5.8 KB
4. Applications of Tensorflow/15. What Now.vtt 5.7 KB
2. Algorithm Overview/15. Multi-Dimensional KNN.vtt 5.5 KB
3. Onwards to Tensorflow JS!/8. Logging Tensor Data.vtt 5.5 KB
13. Performance Optimization/13. Tidying the Training Loop.vtt 5.5 KB
13. Performance Optimization/8. Measuring Footprint Reduction.vtt 5.5 KB
4. Applications of Tensorflow/13. Applying Standardization.vtt 5.4 KB
2. Algorithm Overview/7. Test and Training Data.vtt 5.4 KB
1. What is Machine Learning/8. Recording Observation Data.vtt 5.3 KB
5. Getting Started with Gradient Descent/10. Answering Common Questions.vtt 5.3 KB
11. Multi-Value Classification/3. A Smarter Refactor!.vtt 5.2 KB
2. Algorithm Overview/8. Randomizing Test Data.vtt 5.0 KB
2. Algorithm Overview/9. Generalizing KNN.vtt 5.0 KB
10. Natural Binary Classification/20. Plotting Changing Cost History.vtt 4.9 KB
7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.vtt 4.9 KB
10. Natural Binary Classification/14. Gauging Classification Accuracy.vtt 4.8 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 4.8 KB
13. Performance Optimization/12. Implementing TF Tidy.vtt 4.8 KB
12. Image Recognition In Action/3. Many Features.vtt 4.7 KB
4. Applications of Tensorflow/7. Moving to the Editor.vtt 4.7 KB
11. Multi-Value Classification/13. Calculating Accuracy.vtt 4.5 KB
6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.vtt 4.4 KB
6. Gradient Descent with Tensorflow/7. Updating Coefficients.vtt 4.4 KB
2. Algorithm Overview/11. Printing a Report.vtt 4.4 KB
13. Performance Optimization/7. Releasing References.vtt 4.4 KB
1. What is Machine Learning/5. Problem Outline.vtt 4.3 KB
7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.vtt 4.2 KB
5. Getting Started with Gradient Descent/1. Linear Regression.vtt 4.0 KB
13. Performance Optimization/16. Final Memory Report.vtt 3.9 KB
14. Appendix Custom CSV Loader/3. Reading Files from Disk.vtt 3.9 KB
13. Performance Optimization/11. Cleaning up Tensors with Tidy.vtt 3.9 KB
11. Multi-Value Classification/12. Implementing Accuracy Gauges.vtt 3.8 KB
14. Appendix Custom CSV Loader/4. Splitting into Columns.vtt 3.7 KB
2. Algorithm Overview/24. Evaluating Different Feature Values.vtt 3.7 KB
12. Image Recognition In Action/10. Backfilling Variance.vtt 3.6 KB
10. Natural Binary Classification/1. Introducing Logistic Regression.vtt 3.5 KB
14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.vtt 3.4 KB
13. Performance Optimization/15. One More Optimization.vtt 3.3 KB
11. Multi-Value Classification/1. Multinominal Logistic Regression.vtt 3.2 KB
12. Image Recognition In Action/1. Handwriting Recognition.vtt 3.2 KB
1. What is Machine Learning/4. App Setup.vtt 3.0 KB
14. Appendix Custom CSV Loader/1. Loading CSV Files.vtt 3.0 KB
12. Image Recognition In Action/7. Unchanging Accuracy.vtt 2.9 KB
14. Appendix Custom CSV Loader/2. A Test Dataset.vtt 2.6 KB
13. Performance Optimization/20. Massaging Learning Parameters.vtt 2.4 KB
13. Performance Optimization/9. Optimization Tensorflow Memory Usage.vtt 2.4 KB
13. Performance Optimization/14. Measuring Reduced Memory Usage.vtt 2.2 KB
10. Natural Binary Classification/6. Changes for Logistic Regression.vtt 1.8 KB
1. What is Machine Learning/1. Getting Started - How to Get Help.vtt 1.5 KB
10. Natural Binary Classification/8. Project Download.html 215 bytes
3. Onwards to Tensorflow JS!/4. Tensor Dimension and Shapes.html 139 bytes
3. Onwards to Tensorflow JS!/7. Broadcasting Elementwise Operations.html 139 bytes
[FreeCourseLab.com].url 126 bytes

[FreeCourseLab.com] Udemy - Machine Learning with Javascript

File List

Download Torrent

Related Resources

Copyright Infringement