FreeCourseSite com Udemy Machine Learning with Javascript | 44F562C8175106661AC3EA453E1D198043CD747A

FreeCourseSite com Udemy Machine Learning with Javascript

Name	[FreeCourseSite.com] Udemy - Machine Learning with Javascript	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	11.5 GB
Total Files	373
Hash	44F562C8175106661AC3EA453E1D198043CD747A

/1. What is Machine Learning/
1. Getting Started - How to Get Help.mp4	8.8 MB
1. Getting Started - How to Get Help.vtt	1.6 KB
2. Solving Machine Learning Problems.mp4	65.8 MB
2. Solving Machine Learning Problems.vtt	8.4 KB
3. A Complete Walkthrough.mp4	114.4 MB
3. A Complete Walkthrough.vtt	13.7 KB
4. App Setup.mp4	20.2 MB
4. App Setup.vtt	3.1 KB
5. Problem Outline.mp4	32.7 MB
5. Problem Outline.vtt	4.4 KB
6. Identifying Relevant Data.mp4	35.6 MB
6. Identifying Relevant Data.vtt	6.0 KB
7. Dataset Structures.mp4	50.6 MB
7. Dataset Structures.vtt	8.3 KB
8. Recording Observation Data.mp4	34.3 MB
8. Recording Observation Data.vtt	5.5 KB
9. What Type of Problem.mp4	49.3 MB
9. What Type of Problem.vtt	6.9 KB
/10. Natural Binary Classification/
1. Introducing Logistic Regression.mp4	24.6 MB
1. Introducing Logistic Regression.vtt	3.5 KB
10. Encoding Label Values.mp4	51.0 MB
10. Encoding Label Values.vtt	6.2 KB
11. Updating Linear Regression for Logistic Regression.mp4	73.7 MB
11. Updating Linear Regression for Logistic Regression.vtt	10.0 KB
12. The Sigmoid Equation with Logistic Regression.mp4	34.4 MB
12. The Sigmoid Equation with Logistic Regression.vtt	6.1 KB
13. A Touch More Refactoring.mp4	91.7 MB
13. A Touch More Refactoring.vtt	10.6 KB
14. Gauging Classification Accuracy.mp4	38.5 MB
14. Gauging Classification Accuracy.vtt	4.9 KB
15. Implementing a Test Function.mp4	57.4 MB
15. Implementing a Test Function.vtt	7.6 KB
16. Variable Decision Boundaries.mp4	71.6 MB
16. Variable Decision Boundaries.vtt	10.3 KB
17. Mean Squared Error vs Cross Entropy.mp4	63.1 MB
17. Mean Squared Error vs Cross Entropy.vtt	8.1 KB
18. Refactoring with Cross Entropy.mp4	51.9 MB
18. Refactoring with Cross Entropy.vtt	7.4 KB
19. Finishing the Cost Refactor.mp4	51.5 MB
19. Finishing the Cost Refactor.vtt	6.1 KB
2. Logistic Regression in Action.mp4	64.0 MB
2. Logistic Regression in Action.vtt	9.7 KB
20. Plotting Changing Cost History.mp4	45.0 MB
20. Plotting Changing Cost History.vtt	5.1 KB
3. Bad Equation Fits.mp4	58.1 MB
3. Bad Equation Fits.vtt	7.8 KB
4. The Sigmoid Equation.mp4	47.7 MB
4. The Sigmoid Equation.vtt	6.6 KB
5. Decision Boundaries.mp4	83.0 MB
5. Decision Boundaries.vtt	10.8 KB
6. Changes for Logistic Regression.mp4	13.1 MB
6. Changes for Logistic Regression.vtt	1.8 KB
7. Project Setup for Logistic Regression.mp4	62.3 MB
7. Project Setup for Logistic Regression.vtt	8.3 KB
8. Project Download.html	0.2 KB
8.1 regressions.zip.zip	35.1 KB
9. Importing Vehicle Data.mp4	40.9 MB
9. Importing Vehicle Data.vtt	6.0 KB
/11. Multi-Value Classification/
1. Multinominal Logistic Regression.mp4	26.2 MB
1. Multinominal Logistic Regression.vtt	3.2 KB
10. Sigmoid vs Softmax.mp4	65.8 MB
10. Sigmoid vs Softmax.vtt	8.9 KB
11. Refactoring Sigmoid to Softmax.mp4	51.3 MB
11. Refactoring Sigmoid to Softmax.vtt	6.7 KB
12. Implementing Accuracy Gauges.mp4	30.1 MB
12. Implementing Accuracy Gauges.vtt	3.9 KB
13. Calculating Accuracy.mp4	32.8 MB
13. Calculating Accuracy.vtt	4.6 KB
2. A Smart Refactor to Multinominal Analysis.mp4	52.4 MB
2. A Smart Refactor to Multinominal Analysis.vtt	7.4 KB
3. A Smarter Refactor!.mp4	40.2 MB
3. A Smarter Refactor!.vtt	5.4 KB
4. A Single Instance Approach.mp4	108.6 MB
4. A Single Instance Approach.vtt	108.6 MB
5. Refactoring to Multi-Column Weights.mp4	50.9 MB
5. Refactoring to Multi-Column Weights.vtt	6.9 KB
6. A Problem to Test Multinominal Classification.mp4	50.8 MB
6. A Problem to Test Multinominal Classification.vtt	6.5 KB
7. Classifying Continuous Values.mp4	46.7 MB
7. Classifying Continuous Values.vtt	6.3 KB
8. Training a Multinominal Model.mp4	69.3 MB
8. Training a Multinominal Model.vtt	8.8 KB
9. Marginal vs Conditional Probability.mp4	99.8 MB
9. Marginal vs Conditional Probability.vtt	14.4 KB
/12. Image Recognition In Action/
1. Handwriting Recognition.mp4	25.9 MB
1. Handwriting Recognition.vtt	3.2 KB
10. Backfilling Variance.mp4	27.0 MB
10. Backfilling Variance.vtt	27.0 MB
2. Greyscale Values.mp4	58.0 MB
2. Greyscale Values.vtt	7.2 KB
3. Many Features.mp4	46.9 MB
3. Many Features.vtt	4.8 KB
4. Flattening Image Data.mp4	60.6 MB
4. Flattening Image Data.vtt	8.0 KB
5. Encoding Label Values.mp4	65.0 MB
5. Encoding Label Values.vtt	7.6 KB
6. Implementing an Accuracy Gauge.mp4	83.8 MB
6. Implementing an Accuracy Gauge.vtt	10.3 KB
7. Unchanging Accuracy.mp4	21.3 MB
7. Unchanging Accuracy.vtt	3.0 KB
8. Debugging the Calculation Process.mp4	93.4 MB
8. Debugging the Calculation Process.vtt	11.6 KB
9. Dealing with Zero Variances.mp4	50.2 MB
9. Dealing with Zero Variances.vtt	9.0 KB
/13. Performance Optimization/
1. Handing Large Datasets.mp4	46.6 MB
1. Handing Large Datasets.vtt	6.3 KB
10. Tensorflow's Eager Memory Usage.mp4	49.1 MB
10. Tensorflow's Eager Memory Usage.vtt	6.3 KB
11. Cleaning up Tensors with Tidy.mp4	25.4 MB
11. Cleaning up Tensors with Tidy.vtt	4.0 KB
12. Implementing TF Tidy.mp4	39.4 MB
12. Implementing TF Tidy.vtt	4.9 KB
13. Tidying the Training Loop.mp4	48.2 MB
13. Tidying the Training Loop.vtt	5.6 KB
14. Measuring Reduced Memory Usage.mp4	19.0 MB
14. Measuring Reduced Memory Usage.vtt	2.2 KB
15. One More Optimization.mp4	28.8 MB
15. One More Optimization.vtt	28.8 MB
16. Final Memory Report.mp4	38.0 MB
16. Final Memory Report.vtt	4.0 KB
17. Plotting Cost History.mp4	49.9 MB
17. Plotting Cost History.vtt	5.9 KB
18. NaN in Cost History.mp4	48.6 MB
18. NaN in Cost History.vtt	6.2 KB
19. Fixing Cost History.mp4	49.0 MB
19. Fixing Cost History.vtt	6.4 KB
2. Minimizing Memory Usage.mp4	40.0 MB
2. Minimizing Memory Usage.vtt	6.8 KB
20. Massaging Learning Parameters.mp4	23.7 MB
20. Massaging Learning Parameters.vtt	2.5 KB
21. Improving Model Accuracy.mp4	57.7 MB
21. Improving Model Accuracy.vtt	6.1 KB
3. Creating Memory Snapshots.mp4	51.4 MB
3. Creating Memory Snapshots.vtt	7.3 KB
4. The Javascript Garbage Collector.mp4	58.5 MB
4. The Javascript Garbage Collector.vtt	9.2 KB
5. Shallow vs Retained Memory Usage.mp4	59.7 MB
5. Shallow vs Retained Memory Usage.vtt	8.2 KB
6. Measuring Memory Usage.mp4	101.3 MB
6. Measuring Memory Usage.vtt	12.3 KB
7. Releasing References.mp4	37.7 MB
7. Releasing References.vtt	4.5 KB
8. Measuring Footprint Reduction.mp4	45.4 MB
8. Measuring Footprint Reduction.vtt	5.6 KB
9. Optimization Tensorflow Memory Usage.mp4	19.4 MB
9. Optimization Tensorflow Memory Usage.vtt	2.4 KB
/14. Appendix Custom CSV Loader/
1. Loading CSV Files.mp4	16.6 MB
1. Loading CSV Files.vtt	3.0 KB
10. Splitting Test and Training.mp4	79.3 MB
10. Splitting Test and Training.vtt	10.7 KB
2. A Test Dataset.mp4	10.1 MB
2. A Test Dataset.vtt	2.6 KB
3. Reading Files from Disk.mp4	19.5 MB
3. Reading Files from Disk.vtt	4.0 KB
4. Splitting into Columns.mp4	21.3 MB
4. Splitting into Columns.vtt	3.8 KB
5. Dropping Trailing Columns.mp4	19.3 MB
5. Dropping Trailing Columns.vtt	3.5 KB
6. Parsing Number Values.mp4	32.9 MB
6. Parsing Number Values.vtt	4.9 KB
7. Custom Value Parsing.mp4	38.5 MB
7. Custom Value Parsing.vtt	5.9 KB
8. Extracting Data Columns.mp4	60.1 MB
8. Extracting Data Columns.vtt	7.0 KB
9. Shuffling Data via Seed Phrase.mp4	54.7 MB
9. Shuffling Data via Seed Phrase.vtt	7.6 KB
/2. Algorithm Overview/
1. How K-Nearest Neighbor Works.mp4	97.9 MB
1. How K-Nearest Neighbor Works.vtt	11.7 KB
10. Gauging Accuracy.mp4	56.6 MB
10. Gauging Accuracy.vtt	7.2 KB
11. Printing a Report.mp4	34.9 MB
11. Printing a Report.vtt	4.5 KB
12. Refactoring Accuracy Reporting.mp4	54.8 MB
12. Refactoring Accuracy Reporting.vtt	6.9 KB
13. Investigating Optimal K Values.mp4	135.4 MB
13. Investigating Optimal K Values.vtt	16.1 KB
14. Updating KNN for Multiple Features.mp4	74.1 MB
14. Updating KNN for Multiple Features.vtt	9.3 KB
15. Multi-Dimensional KNN.mp4	46.4 MB
15. Multi-Dimensional KNN.vtt	46.4 MB
16. N-Dimension Distance.mp4	82.7 MB
16. N-Dimension Distance.vtt	13.7 KB
17. Arbitrary Feature Spaces.mp4	74.7 MB
17. Arbitrary Feature Spaces.vtt	12.0 KB
18. Magnitude Offsets in Features.mp4	67.2 MB
18. Magnitude Offsets in Features.vtt	7.8 KB
19. Feature Normalization.mp4	76.5 MB
19. Feature Normalization.vtt	10.4 KB
2. Lodash Review.mp4	68.1 MB
2. Lodash Review.vtt	68.1 MB
20. Normalization with MinMax.mp4	70.3 MB
20. Normalization with MinMax.vtt	9.3 KB
21. Applying Normalization.mp4	47.6 MB
21. Applying Normalization.vtt	6.2 KB
22. Feature Selection with KNN.mp4	84.3 MB
22. Feature Selection with KNN.vtt	11.5 KB
23. Objective Feature Picking.mp4	69.2 MB
23. Objective Feature Picking.vtt	8.4 KB
24. Evaluating Different Feature Values.mp4	29.3 MB
24. Evaluating Different Feature Values.vtt	3.8 KB
3. Implementing KNN.mp4	62.2 MB
3. Implementing KNN.vtt	9.5 KB
4. Finishing KNN Implementation.mp4	52.7 MB
4. Finishing KNN Implementation.vtt	7.9 KB
5. Testing the Algorithm.mp4	47.2 MB
5. Testing the Algorithm.vtt	6.4 KB
6. Interpreting Bad Results.mp4	42.7 MB
6. Interpreting Bad Results.vtt	5.9 KB
7. Test and Training Data.mp4	47.4 MB
7. Test and Training Data.vtt	5.5 KB
8. Randomizing Test Data.mp4	37.8 MB
8. Randomizing Test Data.vtt	5.1 KB
9. Generalizing KNN.mp4	40.9 MB
9. Generalizing KNN.vtt	5.1 KB
/3. Onwards to Tensorflow JS!/
1. Let's Get Our Bearings.mp4	80.3 MB
1. Let's Get Our Bearings.vtt	11.1 KB
10. Creating Slices of Data.mp4	61.8 MB
10. Creating Slices of Data.vtt	10.4 KB
11. Tensor Concatenation.mp4	46.3 MB
11. Tensor Concatenation.vtt	7.6 KB
12. Summing Values Along an Axis.mp4	43.4 MB
12. Summing Values Along an Axis.vtt	7.4 KB
13. Massaging Dimensions with ExpandDims.mp4	59.8 MB
13. Massaging Dimensions with ExpandDims.vtt	11.0 KB
2. A Plan to Move Forward.mp4	51.0 MB
2. A Plan to Move Forward.vtt	7.0 KB
3. Tensor Shape and Dimension.mp4	119.8 MB
3. Tensor Shape and Dimension.vtt	17.1 KB
4. Tensor Dimension and Shapes.html	0.1 KB
5. Elementwise Operations.mp4	61.2 MB
5. Elementwise Operations.vtt	10.7 KB
6. Broadcasting Operations.mp4	65.1 MB
6. Broadcasting Operations.vtt	9.6 KB
7. Broadcasting Elementwise Operations.html	0.1 KB
8. Logging Tensor Data.mp4	27.3 MB
8. Logging Tensor Data.vtt	5.6 KB
9. Tensor Accessors.mp4	31.9 MB
9. Tensor Accessors.vtt	7.7 KB
/4. Applications of Tensorflow/
1. KNN with Regression.mp4	57.7 MB
1. KNN with Regression.vtt	7.2 KB
10. Reporting Error Percentages.mp4	67.6 MB
10. Reporting Error Percentages.vtt	8.4 KB
11. Normalization or Standardization.mp4	97.5 MB
11. Normalization or Standardization.vtt	10.6 KB
12. Numerical Standardization with Tensorflow.mp4	55.6 MB
12. Numerical Standardization with Tensorflow.vtt	10.6 KB
13. Applying Standardization.mp4	43.5 MB
13. Applying Standardization.vtt	43.5 MB
14. Debugging Calculations.mp4	90.9 MB
14. Debugging Calculations.vtt	11.7 KB
15. What Now.mp4	44.4 MB
15. What Now.vtt	5.8 KB
2. A Change in Data Structure.mp4	43.4 MB
2. A Change in Data Structure.vtt	43.4 MB
3. KNN with Tensorflow.mp4	82.5 MB
3. KNN with Tensorflow.vtt	13.3 KB
4. Maintaining Order Relationships.mp4	60.6 MB
4. Maintaining Order Relationships.vtt	9.5 KB
5. Sorting Tensors.mp4	65.9 MB
5. Sorting Tensors.vtt	10.9 KB
6. Averaging Top Values.mp4	61.0 MB
6. Averaging Top Values.vtt	10.5 KB
7. Moving to the Editor.mp4	36.0 MB
7. Moving to the Editor.vtt	4.8 KB
8. Loading CSV Data.mp4	93.7 MB
8. Loading CSV Data.vtt	13.5 KB
9. Running an Analysis.mp4	55.1 MB
9. Running an Analysis.vtt	8.4 KB
/5. Getting Started with Gradient Descent/
1. Linear Regression.mp4	26.6 MB
1. Linear Regression.vtt	26.6 MB
10. Answering Common Questions.mp4	42.9 MB
10. Answering Common Questions.vtt	5.4 KB
11. Gradient Descent with Multiple Terms.mp4	46.4 MB
11. Gradient Descent with Multiple Terms.vtt	6.7 KB
12. Multiple Terms in Action.mp4	129.1 MB
12. Multiple Terms in Action.vtt	129.2 MB
2. Why Linear Regression.mp4	52.8 MB
2. Why Linear Regression.vtt	6.9 KB
3. Understanding Gradient Descent.mp4	132.9 MB
3. Understanding Gradient Descent.vtt	17.4 KB
4. Guessing Coefficients with MSE.mp4	98.0 MB
4. Guessing Coefficients with MSE.vtt	13.9 KB
5. Observations Around MSE.mp4	58.8 MB
5. Observations Around MSE.vtt	8.4 KB
6. Derivatives!.mp4	81.7 MB
6. Derivatives!.vtt	81.7 MB
7. Gradient Descent in Action.mp4	121.0 MB
7. Gradient Descent in Action.vtt	16.5 KB
8. Quick Breather and Review.mp4	69.0 MB
8. Quick Breather and Review.vtt	8.2 KB
9. Why a Learning Rate.mp4	196.4 MB
9. Why a Learning Rate.vtt	23.2 KB
/6. Gradient Descent with Tensorflow/
1. Project Overview.mp4	59.8 MB
1. Project Overview.vtt	48.4 MB
10. More on Matrix Multiplication.mp4	66.3 MB
10. More on Matrix Multiplication.vtt	8.4 KB
11. Matrix Form of Slope Equations.mp4	62.5 MB
11. Matrix Form of Slope Equations.vtt	8.7 KB
12. Simplification with Matrix Multiplication.mp4	95.2 MB
12. Simplification with Matrix Multiplication.vtt	12.9 KB
13. How it All Works Together!.mp4	150.8 MB
13. How it All Works Together!.vtt	18.7 KB
2. Data Loading.mp4	45.6 MB
2. Data Loading.vtt	7.0 KB
3. Default Algorithm Options.mp4	65.7 MB
3. Default Algorithm Options.vtt	11.5 KB
4. Formulating the Training Loop.mp4	29.0 MB
4. Formulating the Training Loop.vtt	4.5 KB
5. Initial Gradient Descent Implementation.mp4	92.2 MB
5. Initial Gradient Descent Implementation.vtt	12.8 KB
6. Calculating MSE Slopes.mp4	70.4 MB
6. Calculating MSE Slopes.vtt	8.7 KB
7. Updating Coefficients.mp4	35.5 MB
7. Updating Coefficients.vtt	35.5 MB
8. Interpreting Results.mp4	106.7 MB
8. Interpreting Results.vtt	13.9 KB
9. Matrix Multiplication.mp4	70.7 MB
9. Matrix Multiplication.vtt	10.1 KB
/7. Increasing Performance with Vectorized Solutions/
1. Refactoring the Linear Regression Class.mp4	76.3 MB
1. Refactoring the Linear Regression Class.vtt	10.5 KB
10. Reapplying Standardization.mp4	60.8 MB
10. Reapplying Standardization.vtt	7.7 KB
11. Fixing Standardization Issues.mp4	50.2 MB
11. Fixing Standardization Issues.vtt	8.1 KB
12. Massaging Learning Rates.mp4	38.2 MB
12. Massaging Learning Rates.vtt	4.3 KB
13. Moving Towards Multivariate Regression.mp4	127.3 MB
13. Moving Towards Multivariate Regression.vtt	16.3 KB
14. Refactoring for Multivariate Analysis.mp4	86.4 MB
14. Refactoring for Multivariate Analysis.vtt	10.7 KB
15. Learning Rate Optimization.mp4	80.4 MB
15. Learning Rate Optimization.vtt	11.3 KB
16. Recording MSE History.mp4	54.5 MB
16. Recording MSE History.vtt	7.3 KB
17. Updating Learning Rate.mp4	65.2 MB
17. Updating Learning Rate.vtt	9.1 KB
2. Refactoring to One Equation.mp4	88.9 MB
2. Refactoring to One Equation.vtt	12.5 KB
3. A Few More Changes.mp4	69.4 MB
3. A Few More Changes.vtt	9.1 KB
4. Same Results Or Not.mp4	35.5 MB
4. Same Results Or Not.vtt	4.9 KB
5. Calculating Model Accuracy.mp4	84.3 MB
5. Calculating Model Accuracy.vtt	11.9 KB
6. Implementing Coefficient of Determination.mp4	79.5 MB
6. Implementing Coefficient of Determination.vtt	10.6 KB
7. Dealing with Bad Accuracy.mp4	74.9 MB
7. Dealing with Bad Accuracy.vtt	10.7 KB
8. Reminder on Standardization.mp4	46.7 MB
8. Reminder on Standardization.vtt	6.3 KB
9. Data Processing in a Helper Method.mp4	39.0 MB
9. Data Processing in a Helper Method.vtt	5.0 KB
/8. Plotting Data with Javascript/
1. Observing Changing Learning Rate and MSE.mp4	48.1 MB
1. Observing Changing Learning Rate and MSE.vtt	6.1 KB
2. Plotting MSE Values.mp4	64.4 MB
2. Plotting MSE Values.vtt	7.4 KB
3. Plotting MSE History against B Values.mp4	50.1 MB
3. Plotting MSE History against B Values.vtt	6.4 KB
/9. Gradient Descent Alterations/
1. Batch and Stochastic Gradient Descent.mp4	81.0 MB
1. Batch and Stochastic Gradient Descent.vtt	10.4 KB
2. Refactoring Towards Batch Gradient Descent.mp4	57.8 MB
2. Refactoring Towards Batch Gradient Descent.vtt	7.3 KB
3. Determining Batch Size and Quantity.mp4	69.3 MB
3. Determining Batch Size and Quantity.vtt	8.0 KB
4. Iterating Over Batches.mp4	70.7 MB
4. Iterating Over Batches.vtt	10.8 KB
5. Evaluating Batch Gradient Descent Results.mp4	69.5 MB
5. Evaluating Batch Gradient Descent Results.vtt	8.2 KB
6. Making Predictions with the Model.mp4	83.3 MB
6. Making Predictions with the Model.vtt	10.7 KB
/
[CourseClub.NET].url	0.1 KB
[FCS Forum].url	0.1 KB
[FreeCourseSite.com].url	0.1 KB
Total files 373