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[FreeCourseSite.com] Udemy - Machine Learning with Javascript

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视频 2020-2-10 07:07 2024-12-27 06:10 126 10.1 GB 183
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FreeCourseSite com Udemy - Machine Learning with Javascript

文件列表
  1. 1. What is Machine Learning/1. Getting Started - How to Get Help.mp48.36MB
  2. 1. What is Machine Learning/2. Solving Machine Learning Problems.mp462.78MB
  3. 1. What is Machine Learning/3. A Complete Walkthrough.mp4109.14MB
  4. 1. What is Machine Learning/4. App Setup.mp419.27MB
  5. 1. What is Machine Learning/5. Problem Outline.mp431.22MB
  6. 1. What is Machine Learning/6. Identifying Relevant Data.mp433.91MB
  7. 1. What is Machine Learning/7. Dataset Structures.mp448.25MB
  8. 1. What is Machine Learning/8. Recording Observation Data.mp432.74MB
  9. 1. What is Machine Learning/9. What Type of Problem.mp447.04MB
  10. 10. Natural Binary Classification/1. Introducing Logistic Regression.mp423.45MB
  11. 10. Natural Binary Classification/10. Encoding Label Values.mp448.59MB
  12. 10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.mp470.29MB
  13. 10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.mp432.78MB
  14. 10. Natural Binary Classification/13. A Touch More Refactoring.mp487.43MB
  15. 10. Natural Binary Classification/14. Gauging Classification Accuracy.mp436.71MB
  16. 10. Natural Binary Classification/15. Implementing a Test Function.mp454.71MB
  17. 10. Natural Binary Classification/16. Variable Decision Boundaries.mp468.32MB
  18. 10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.mp460.2MB
  19. 10. Natural Binary Classification/18. Refactoring with Cross Entropy.mp449.46MB
  20. 10. Natural Binary Classification/19. Finishing the Cost Refactor.mp449.1MB
  21. 10. Natural Binary Classification/2. Logistic Regression in Action.mp461.07MB
  22. 10. Natural Binary Classification/20. Plotting Changing Cost History.mp442.95MB
  23. 10. Natural Binary Classification/3. Bad Equation Fits.mp455.39MB
  24. 10. Natural Binary Classification/4. The Sigmoid Equation.mp445.45MB
  25. 10. Natural Binary Classification/5. Decision Boundaries.mp479.18MB
  26. 10. Natural Binary Classification/6. Changes for Logistic Regression.mp412.5MB
  27. 10. Natural Binary Classification/7. Project Setup for Logistic Regression.mp459.41MB
  28. 10. Natural Binary Classification/9. Importing Vehicle Data.mp438.96MB
  29. 11. Multi-Value Classification/1. Multinominal Logistic Regression.mp425MB
  30. 11. Multi-Value Classification/10. Sigmoid vs Softmax.mp462.76MB
  31. 11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.mp448.88MB
  32. 11. Multi-Value Classification/12. Implementing Accuracy Gauges.mp428.72MB
  33. 11. Multi-Value Classification/13. Calculating Accuracy.mp431.31MB
  34. 11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.mp449.97MB
  35. 11. Multi-Value Classification/3. A Smarter Refactor!.mp438.3MB
  36. 11. Multi-Value Classification/4. A Single Instance Approach.mp4103.56MB
  37. 11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.mp448.5MB
  38. 11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.mp448.46MB
  39. 11. Multi-Value Classification/7. Classifying Continuous Values.mp444.56MB
  40. 11. Multi-Value Classification/8. Training a Multinominal Model.mp466.09MB
  41. 11. Multi-Value Classification/9. Marginal vs Conditional Probability.mp495.19MB
  42. 12. Image Recognition In Action/1. Handwriting Recognition.mp424.7MB
  43. 12. Image Recognition In Action/10. Backfilling Variance.mp425.73MB
  44. 12. Image Recognition In Action/2. Greyscale Values.mp455.35MB
  45. 12. Image Recognition In Action/3. Many Features.mp444.77MB
  46. 12. Image Recognition In Action/4. Flattening Image Data.mp457.77MB
  47. 12. Image Recognition In Action/5. Encoding Label Values.mp462.01MB
  48. 12. Image Recognition In Action/6. Implementing an Accuracy Gauge.mp479.95MB
  49. 12. Image Recognition In Action/7. Unchanging Accuracy.mp420.3MB
  50. 12. Image Recognition In Action/8. Debugging the Calculation Process.mp489.05MB
  51. 12. Image Recognition In Action/9. Dealing with Zero Variances.mp447.91MB
  52. 13. Performance Optimization/1. Handing Large Datasets.mp444.47MB
  53. 13. Performance Optimization/10. Tensorflow's Eager Memory Usage.mp446.81MB
  54. 13. Performance Optimization/11. Cleaning up Tensors with Tidy.mp424.27MB
  55. 13. Performance Optimization/12. Implementing TF Tidy.mp437.6MB
  56. 13. Performance Optimization/13. Tidying the Training Loop.mp445.99MB
  57. 13. Performance Optimization/14. Measuring Reduced Memory Usage.mp418.12MB
  58. 13. Performance Optimization/15. One More Optimization.mp427.5MB
  59. 13. Performance Optimization/16. Final Memory Report.mp436.25MB
  60. 13. Performance Optimization/17. Plotting Cost History.mp447.6MB
  61. 13. Performance Optimization/18. NaN in Cost History.mp446.37MB
  62. 13. Performance Optimization/19. Fixing Cost History.mp446.78MB
  63. 13. Performance Optimization/2. Minimizing Memory Usage.mp438.19MB
  64. 13. Performance Optimization/20. Massaging Learning Parameters.mp422.56MB
  65. 13. Performance Optimization/21. Improving Model Accuracy.mp455.02MB
  66. 13. Performance Optimization/3. Creating Memory Snapshots.mp449.06MB
  67. 13. Performance Optimization/4. The Javascript Garbage Collector.mp455.81MB
  68. 13. Performance Optimization/5. Shallow vs Retained Memory Usage.mp456.9MB
  69. 13. Performance Optimization/6. Measuring Memory Usage.mp496.64MB
  70. 13. Performance Optimization/7. Releasing References.mp435.98MB
  71. 13. Performance Optimization/8. Measuring Footprint Reduction.mp443.31MB
  72. 13. Performance Optimization/9. Optimization Tensorflow Memory Usage.mp418.54MB
  73. 14. Appendix Custom CSV Loader/1. Loading CSV Files.mp415.86MB
  74. 14. Appendix Custom CSV Loader/10. Splitting Test and Training.mp475.66MB
  75. 14. Appendix Custom CSV Loader/2. A Test Dataset.mp49.59MB
  76. 14. Appendix Custom CSV Loader/3. Reading Files from Disk.mp418.6MB
  77. 14. Appendix Custom CSV Loader/4. Splitting into Columns.mp420.35MB
  78. 14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.mp418.41MB
  79. 14. Appendix Custom CSV Loader/6. Parsing Number Values.mp431.37MB
  80. 14. Appendix Custom CSV Loader/7. Custom Value Parsing.mp436.72MB
  81. 14. Appendix Custom CSV Loader/8. Extracting Data Columns.mp457.28MB
  82. 14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.mp452.14MB
  83. 2. Algorithm Overview/1. How K-Nearest Neighbor Works.mp493.33MB
  84. 2. Algorithm Overview/10. Gauging Accuracy.mp454.02MB
  85. 2. Algorithm Overview/11. Printing a Report.mp433.3MB
  86. 2. Algorithm Overview/12. Refactoring Accuracy Reporting.mp452.31MB
  87. 2. Algorithm Overview/13. Investigating Optimal K Values.mp4129.14MB
  88. 2. Algorithm Overview/14. Updating KNN for Multiple Features.mp470.62MB
  89. 2. Algorithm Overview/15. Multi-Dimensional KNN.mp444.21MB
  90. 2. Algorithm Overview/16. N-Dimension Distance.mp478.88MB
  91. 2. Algorithm Overview/17. Arbitrary Feature Spaces.mp471.26MB
  92. 2. Algorithm Overview/18. Magnitude Offsets in Features.mp464.06MB
  93. 2. Algorithm Overview/19. Feature Normalization.mp472.92MB
  94. 2. Algorithm Overview/2. Lodash Review.mp464.94MB
  95. 2. Algorithm Overview/20. Normalization with MinMax.mp467.05MB
  96. 2. Algorithm Overview/21. Applying Normalization.mp445.36MB
  97. 2. Algorithm Overview/22. Feature Selection with KNN.mp480.37MB
  98. 2. Algorithm Overview/23. Objective Feature Picking.mp465.98MB
  99. 2. Algorithm Overview/24. Evaluating Different Feature Values.mp427.97MB
  100. 2. Algorithm Overview/3. Implementing KNN.mp459.34MB
  101. 2. Algorithm Overview/4. Finishing KNN Implementation.mp450.28MB
  102. 2. Algorithm Overview/5. Testing the Algorithm.mp444.97MB
  103. 2. Algorithm Overview/6. Interpreting Bad Results.mp440.76MB
  104. 2. Algorithm Overview/7. Test and Training Data.mp445.21MB
  105. 2. Algorithm Overview/8. Randomizing Test Data.mp436.01MB
  106. 2. Algorithm Overview/9. Generalizing KNN.mp439MB
  107. 3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.mp476.62MB
  108. 3. Onwards to Tensorflow JS!/10. Creating Slices of Data.mp458.92MB
  109. 3. Onwards to Tensorflow JS!/11. Tensor Concatenation.mp444.14MB
  110. 3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.mp441.37MB
  111. 3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.mp457.02MB
  112. 3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.mp448.66MB
  113. 3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.mp4114.29MB
  114. 3. Onwards to Tensorflow JS!/5. Elementwise Operations.mp458.36MB
  115. 3. Onwards to Tensorflow JS!/6. Broadcasting Operations.mp462.06MB
  116. 3. Onwards to Tensorflow JS!/8. Logging Tensor Data.mp426MB
  117. 3. Onwards to Tensorflow JS!/9. Tensor Accessors.mp430.46MB
  118. 4. Applications of Tensorflow/1. KNN with Regression.mp454.99MB
  119. 4. Applications of Tensorflow/10. Reporting Error Percentages.mp464.5MB
  120. 4. Applications of Tensorflow/11. Normalization or Standardization.mp492.97MB
  121. 4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.mp453.06MB
  122. 4. Applications of Tensorflow/13. Applying Standardization.mp441.47MB
  123. 4. Applications of Tensorflow/14. Debugging Calculations.mp486.72MB
  124. 4. Applications of Tensorflow/15. What Now.mp442.33MB
  125. 4. Applications of Tensorflow/2. A Change in Data Structure.mp441.35MB
  126. 4. Applications of Tensorflow/3. KNN with Tensorflow.mp478.72MB
  127. 4. Applications of Tensorflow/4. Maintaining Order Relationships.mp457.76MB
  128. 4. Applications of Tensorflow/5. Sorting Tensors.mp462.85MB
  129. 4. Applications of Tensorflow/6. Averaging Top Values.mp458.13MB
  130. 4. Applications of Tensorflow/7. Moving to the Editor.mp434.33MB
  131. 4. Applications of Tensorflow/8. Loading CSV Data.mp489.33MB
  132. 4. Applications of Tensorflow/9. Running an Analysis.mp452.5MB
  133. 5. Getting Started with Gradient Descent/1. Linear Regression.mp425.39MB
  134. 5. Getting Started with Gradient Descent/10. Answering Common Questions.mp440.95MB
  135. 5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.mp444.2MB
  136. 5. Getting Started with Gradient Descent/12. Multiple Terms in Action.mp4123.16MB
  137. 5. Getting Started with Gradient Descent/2. Why Linear Regression.mp450.35MB
  138. 5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.mp4126.77MB
  139. 5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.mp493.47MB
  140. 5. Getting Started with Gradient Descent/5. Observations Around MSE.mp456.11MB
  141. 5. Getting Started with Gradient Descent/6. Derivatives!.mp477.96MB
  142. 5. Getting Started with Gradient Descent/7. Gradient Descent in Action.mp4115.36MB
  143. 5. Getting Started with Gradient Descent/8. Quick Breather and Review.mp465.8MB
  144. 5. Getting Started with Gradient Descent/9. Why a Learning Rate.mp4187.28MB
  145. 6. Gradient Descent with Tensorflow/1. Project Overview.mp457.04MB
  146. 6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.mp463.25MB
  147. 6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.mp459.6MB
  148. 6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.mp490.8MB
  149. 6. Gradient Descent with Tensorflow/13. How it All Works Together!.mp4143.82MB
  150. 6. Gradient Descent with Tensorflow/2. Data Loading.mp443.49MB
  151. 6. Gradient Descent with Tensorflow/3. Default Algorithm Options.mp462.66MB
  152. 6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.mp427.68MB
  153. 6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.mp487.93MB
  154. 6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.mp467.14MB
  155. 6. Gradient Descent with Tensorflow/7. Updating Coefficients.mp433.86MB
  156. 6. Gradient Descent with Tensorflow/8. Interpreting Results.mp4101.71MB
  157. 6. Gradient Descent with Tensorflow/9. Matrix Multiplication.mp467.47MB
  158. 7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.mp472.72MB
  159. 7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.mp457.96MB
  160. 7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.mp447.85MB
  161. 7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.mp436.44MB
  162. 7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.mp4121.42MB
  163. 7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.mp482.36MB
  164. 7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.mp476.69MB
  165. 7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.mp451.95MB
  166. 7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.mp462.15MB
  167. 7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.mp484.81MB
  168. 7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.mp466.16MB
  169. 7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.mp433.84MB
  170. 7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.mp480.37MB
  171. 7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.mp475.79MB
  172. 7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.mp471.42MB
  173. 7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.mp444.5MB
  174. 7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.mp437.18MB
  175. 8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.mp445.84MB
  176. 8. Plotting Data with Javascript/2. Plotting MSE Values.mp461.4MB
  177. 8. Plotting Data with Javascript/3. Plotting MSE History against B Values.mp447.81MB
  178. 9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.mp477.24MB
  179. 9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.mp455.11MB
  180. 9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.mp466.09MB
  181. 9. Gradient Descent Alterations/4. Iterating Over Batches.mp467.46MB
  182. 9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.mp466.24MB
  183. 9. Gradient Descent Alterations/6. Making Predictions with the Model.mp479.49MB
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