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种子名称： [FreeCourseLab.com] Udemy - Machine Learning with Javascript
文件类型： 视频
文件数目： 183个文件
文件大小： 10.1 GB
收录时间： 2019-6-10 19:52
已经下载： 3次
资源热度： 147
最近下载： 2024-11-20 01:05

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[FreeCourseLab.com] Udemy - Machine Learning with Javascript.torrent

1. What is Machine Learning/1. Getting Started - How to Get Help.mp48.35MB

1. What is Machine Learning/2. Solving Machine Learning Problems.mp462.77MB

1. What is Machine Learning/3. A Complete Walkthrough.mp4109.13MB

1. What is Machine Learning/4. App Setup.mp419.27MB

1. What is Machine Learning/5. Problem Outline.mp431.21MB

1. What is Machine Learning/6. Identifying Relevant Data.mp433.91MB

1. What is Machine Learning/7. Dataset Structures.mp448.25MB

1. What is Machine Learning/8. Recording Observation Data.mp432.74MB

1. What is Machine Learning/9. What Type of Problem.mp447.03MB

10. Natural Binary Classification/1. Introducing Logistic Regression.mp423.45MB

10. Natural Binary Classification/10. Encoding Label Values.mp448.58MB

10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.mp470.3MB

10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.mp432.78MB

10. Natural Binary Classification/13. A Touch More Refactoring.mp487.42MB

10. Natural Binary Classification/14. Gauging Classification Accuracy.mp436.7MB

10. Natural Binary Classification/15. Implementing a Test Function.mp454.71MB

10. Natural Binary Classification/16. Variable Decision Boundaries.mp468.32MB

10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.mp460.2MB

10. Natural Binary Classification/18. Refactoring with Cross Entropy.mp449.45MB

10. Natural Binary Classification/19. Finishing the Cost Refactor.mp449.09MB

10. Natural Binary Classification/2. Logistic Regression in Action.mp461.07MB

10. Natural Binary Classification/20. Plotting Changing Cost History.mp442.95MB

10. Natural Binary Classification/3. Bad Equation Fits.mp455.4MB

10. Natural Binary Classification/4. The Sigmoid Equation.mp445.44MB

10. Natural Binary Classification/5. Decision Boundaries.mp479.18MB

10. Natural Binary Classification/6. Changes for Logistic Regression.mp412.49MB

10. Natural Binary Classification/7. Project Setup for Logistic Regression.mp459.4MB

10. Natural Binary Classification/9. Importing Vehicle Data.mp438.95MB

11. Multi-Value Classification/1. Multinominal Logistic Regression.mp425MB

11. Multi-Value Classification/10. Sigmoid vs Softmax.mp462.75MB

11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.mp448.87MB

11. Multi-Value Classification/12. Implementing Accuracy Gauges.mp428.71MB

11. Multi-Value Classification/13. Calculating Accuracy.mp431.3MB

11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.mp449.97MB

11. Multi-Value Classification/3. A Smarter Refactor!.mp438.29MB

11. Multi-Value Classification/4. A Single Instance Approach.mp4103.56MB

11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.mp448.49MB

11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.mp448.45MB

11. Multi-Value Classification/7. Classifying Continuous Values.mp444.55MB

11. Multi-Value Classification/8. Training a Multinominal Model.mp466.09MB

11. Multi-Value Classification/9. Marginal vs Conditional Probability.mp495.18MB

12. Image Recognition In Action/1. Handwriting Recognition.mp424.69MB

12. Image Recognition In Action/10. Backfilling Variance.mp425.72MB

12. Image Recognition In Action/2. Greyscale Values.mp455.35MB

12. Image Recognition In Action/3. Many Features.mp444.76MB

12. Image Recognition In Action/4. Flattening Image Data.mp457.76MB

12. Image Recognition In Action/5. Encoding Label Values.mp462MB

12. Image Recognition In Action/6. Implementing an Accuracy Gauge.mp479.94MB

12. Image Recognition In Action/7. Unchanging Accuracy.mp420.3MB

12. Image Recognition In Action/8. Debugging the Calculation Process.mp489.05MB

12. Image Recognition In Action/9. Dealing with Zero Variances.mp447.9MB

13. Performance Optimization/1. Handing Large Datasets.mp444.46MB

13. Performance Optimization/10. Tensorflow's Eager Memory Usage.mp446.81MB

13. Performance Optimization/11. Cleaning up Tensors with Tidy.mp424.26MB

13. Performance Optimization/12. Implementing TF Tidy.mp437.6MB

13. Performance Optimization/13. Tidying the Training Loop.mp445.99MB

13. Performance Optimization/14. Measuring Reduced Memory Usage.mp418.11MB

13. Performance Optimization/15. One More Optimization.mp427.5MB

13. Performance Optimization/16. Final Memory Report.mp436.25MB

13. Performance Optimization/17. Plotting Cost History.mp447.6MB

13. Performance Optimization/18. NaN in Cost History.mp446.38MB

13. Performance Optimization/19. Fixing Cost History.mp446.77MB

13. Performance Optimization/2. Minimizing Memory Usage.mp438.19MB

13. Performance Optimization/20. Massaging Learning Parameters.mp422.55MB

13. Performance Optimization/21. Improving Model Accuracy.mp455.02MB

13. Performance Optimization/3. Creating Memory Snapshots.mp449.06MB

13. Performance Optimization/4. The Javascript Garbage Collector.mp455.8MB

13. Performance Optimization/5. Shallow vs Retained Memory Usage.mp456.89MB

13. Performance Optimization/6. Measuring Memory Usage.mp496.63MB

13. Performance Optimization/7. Releasing References.mp435.98MB

13. Performance Optimization/8. Measuring Footprint Reduction.mp443.31MB

13. Performance Optimization/9. Optimization Tensorflow Memory Usage.mp418.54MB

14. Appendix Custom CSV Loader/1. Loading CSV Files.mp415.86MB

14. Appendix Custom CSV Loader/10. Splitting Test and Training.mp475.65MB

14. Appendix Custom CSV Loader/2. A Test Dataset.mp49.58MB

14. Appendix Custom CSV Loader/3. Reading Files from Disk.mp418.6MB

14. Appendix Custom CSV Loader/4. Splitting into Columns.mp420.34MB

14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.mp418.4MB

14. Appendix Custom CSV Loader/6. Parsing Number Values.mp431.36MB

14. Appendix Custom CSV Loader/7. Custom Value Parsing.mp436.73MB

14. Appendix Custom CSV Loader/8. Extracting Data Columns.mp457.27MB

14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.mp452.14MB

2. Algorithm Overview/1. How K-Nearest Neighbor Works.mp493.33MB

2. Algorithm Overview/10. Gauging Accuracy.mp454.02MB

2. Algorithm Overview/11. Printing a Report.mp433.3MB

2. Algorithm Overview/12. Refactoring Accuracy Reporting.mp452.3MB

2. Algorithm Overview/13. Investigating Optimal K Values.mp4129.14MB

2. Algorithm Overview/14. Updating KNN for Multiple Features.mp470.61MB

2. Algorithm Overview/15. Multi-Dimensional KNN.mp444.2MB

2. Algorithm Overview/16. N-Dimension Distance.mp478.87MB

2. Algorithm Overview/17. Arbitrary Feature Spaces.mp471.25MB

2. Algorithm Overview/18. Magnitude Offsets in Features.mp464.06MB

2. Algorithm Overview/19. Feature Normalization.mp472.92MB

2. Algorithm Overview/2. Lodash Review.mp464.93MB

2. Algorithm Overview/20. Normalization with MinMax.mp467.04MB

2. Algorithm Overview/21. Applying Normalization.mp445.35MB

2. Algorithm Overview/22. Feature Selection with KNN.mp480.37MB

2. Algorithm Overview/23. Objective Feature Picking.mp465.97MB

2. Algorithm Overview/24. Evaluating Different Feature Values.mp427.97MB

2. Algorithm Overview/3. Implementing KNN.mp459.34MB

2. Algorithm Overview/4. Finishing KNN Implementation.mp450.28MB

2. Algorithm Overview/5. Testing the Algorithm.mp444.97MB

2. Algorithm Overview/6. Interpreting Bad Results.mp440.75MB

2. Algorithm Overview/7. Test and Training Data.mp445.2MB

2. Algorithm Overview/8. Randomizing Test Data.mp436.01MB

2. Algorithm Overview/9. Generalizing KNN.mp439MB

3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.mp476.62MB

3. Onwards to Tensorflow JS!/10. Creating Slices of Data.mp458.91MB

3. Onwards to Tensorflow JS!/11. Tensor Concatenation.mp444.13MB

3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.mp441.36MB

3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.mp457.02MB

3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.mp448.65MB

3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.mp4114.28MB

3. Onwards to Tensorflow JS!/5. Elementwise Operations.mp458.36MB

3. Onwards to Tensorflow JS!/6. Broadcasting Operations.mp462.06MB

3. Onwards to Tensorflow JS!/8. Logging Tensor Data.mp426MB

3. Onwards to Tensorflow JS!/9. Tensor Accessors.mp430.47MB

4. Applications of Tensorflow/1. KNN with Regression.mp454.99MB

4. Applications of Tensorflow/10. Reporting Error Percentages.mp464.5MB

4. Applications of Tensorflow/11. Normalization or Standardization.mp492.98MB

4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.mp453.05MB

4. Applications of Tensorflow/13. Applying Standardization.mp441.46MB

4. Applications of Tensorflow/14. Debugging Calculations.mp486.71MB

4. Applications of Tensorflow/15. What Now.mp442.33MB

4. Applications of Tensorflow/2. A Change in Data Structure.mp441.35MB

4. Applications of Tensorflow/3. KNN with Tensorflow.mp478.71MB

4. Applications of Tensorflow/4. Maintaining Order Relationships.mp457.75MB

4. Applications of Tensorflow/5. Sorting Tensors.mp462.85MB

4. Applications of Tensorflow/6. Averaging Top Values.mp458.13MB

4. Applications of Tensorflow/7. Moving to the Editor.mp434.34MB

4. Applications of Tensorflow/8. Loading CSV Data.mp489.33MB

4. Applications of Tensorflow/9. Running an Analysis.mp452.5MB

5. Getting Started with Gradient Descent/1. Linear Regression.mp425.38MB

5. Getting Started with Gradient Descent/10. Answering Common Questions.mp440.94MB

5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.mp444.2MB

5. Getting Started with Gradient Descent/12. Multiple Terms in Action.mp4123.16MB

5. Getting Started with Gradient Descent/2. Why Linear Regression.mp450.34MB

5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.mp4126.77MB

5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.mp493.47MB

5. Getting Started with Gradient Descent/5. Observations Around MSE.mp456.12MB

5. Getting Started with Gradient Descent/6. Derivatives!.mp477.95MB

5. Getting Started with Gradient Descent/7. Gradient Descent in Action.mp4115.36MB

5. Getting Started with Gradient Descent/8. Quick Breather and Review.mp465.79MB

5. Getting Started with Gradient Descent/9. Why a Learning Rate.mp4187.28MB

6. Gradient Descent with Tensorflow/1. Project Overview.mp457.05MB

6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.mp463.25MB

6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.mp459.61MB

6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.mp490.79MB

6. Gradient Descent with Tensorflow/13. How it All Works Together!.mp4143.82MB

6. Gradient Descent with Tensorflow/2. Data Loading.mp443.49MB

6. Gradient Descent with Tensorflow/3. Default Algorithm Options.mp462.66MB

6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.mp427.68MB

6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.mp487.93MB

6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.mp467.14MB

6. Gradient Descent with Tensorflow/7. Updating Coefficients.mp433.86MB

6. Gradient Descent with Tensorflow/8. Interpreting Results.mp4101.72MB

6. Gradient Descent with Tensorflow/9. Matrix Multiplication.mp467.46MB

7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.mp472.71MB

7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.mp457.96MB

7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.mp447.84MB

7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.mp436.45MB

7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.mp4121.42MB

7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.mp482.36MB

7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.mp476.69MB

7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.mp451.95MB

7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.mp462.15MB

7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.mp484.8MB

7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.mp466.17MB

7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.mp433.83MB

7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.mp480.37MB

7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.mp475.79MB

7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.mp471.41MB

7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.mp444.49MB

7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.mp437.17MB

8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.mp445.83MB

8. Plotting Data with Javascript/2. Plotting MSE Values.mp461.4MB

8. Plotting Data with Javascript/3. Plotting MSE History against B Values.mp447.81MB

9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.mp477.24MB

9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.mp455.11MB

9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.mp466.09MB

9. Gradient Descent Alterations/4. Iterating Over Batches.mp467.45MB

9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.mp466.23MB

9. Gradient Descent Alterations/6. Making Predictions with the Model.mp479.5MB