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[FreeCourseSite.com] Udemy - Machine Learning with Javascript

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视频 2024-3-15 06:07 2024-12-26 01:07 161 6.75 GB 183
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FreeCourseSite com Udemy - Machine Learning with Javascript

文件列表
  1. 01 - What is Machine Learning/001 Getting Started - How to Get Help.mp48.35MB
  2. 01 - What is Machine Learning/004 Solving Machine Learning Problems.mp462.77MB
  3. 01 - What is Machine Learning/005 A Complete Walkthrough.mp463.3MB
  4. 01 - What is Machine Learning/006 App Setup.mp48.13MB
  5. 01 - What is Machine Learning/007 Problem Outline.mp422.9MB
  6. 01 - What is Machine Learning/008 Identifying Relevant Data.mp433.91MB
  7. 01 - What is Machine Learning/009 Dataset Structures.mp448.24MB
  8. 01 - What is Machine Learning/010 Recording Observation Data.mp412.68MB
  9. 01 - What is Machine Learning/011 What Type of Problem.mp416.86MB
  10. 02 - Algorithm Overview/001 How K-Nearest Neighbor Works.mp469.73MB
  11. 02 - Algorithm Overview/002 Lodash Review.mp448.26MB
  12. 02 - Algorithm Overview/003 Implementing KNN.mp459.34MB
  13. 02 - Algorithm Overview/004 Finishing KNN Implementation.mp437.96MB
  14. 02 - Algorithm Overview/005 Testing the Algorithm.mp444.96MB
  15. 02 - Algorithm Overview/006 Interpreting Bad Results.mp425.65MB
  16. 02 - Algorithm Overview/007 Test and Training Data.mp427.37MB
  17. 02 - Algorithm Overview/008 Randomizing Test Data.mp413.46MB
  18. 02 - Algorithm Overview/009 Generalizing KNN.mp438.99MB
  19. 02 - Algorithm Overview/010 Gauging Accuracy.mp445.97MB
  20. 02 - Algorithm Overview/011 Printing a Report.mp433.29MB
  21. 02 - Algorithm Overview/012 Refactoring Accuracy Reporting.mp433.81MB
  22. 02 - Algorithm Overview/013 Investigating Optimal K Values.mp4112.41MB
  23. 02 - Algorithm Overview/014 Updating KNN for Multiple Features.mp448.83MB
  24. 02 - Algorithm Overview/015 Multi-Dimensional KNN.mp431.92MB
  25. 02 - Algorithm Overview/016 N-Dimension Distance.mp459.47MB
  26. 02 - Algorithm Overview/017 Arbitrary Feature Spaces.mp458.1MB
  27. 02 - Algorithm Overview/018 Magnitude Offsets in Features.mp446.23MB
  28. 02 - Algorithm Overview/019 Feature Normalization.mp454.09MB
  29. 02 - Algorithm Overview/020 Normalization with MinMax.mp454.4MB
  30. 02 - Algorithm Overview/021 Applying Normalization.mp438.91MB
  31. 02 - Algorithm Overview/022 Feature Selection with KNN.mp457.3MB
  32. 02 - Algorithm Overview/023 Objective Feature Picking.mp428.47MB
  33. 02 - Algorithm Overview/024 Evaluating Different Feature Values.mp421.01MB
  34. 03 - Onwards to Tensorflow JS!/001 Let's Get Our Bearings.mp446.8MB
  35. 03 - Onwards to Tensorflow JS!/002 A Plan to Move Forward.mp436.9MB
  36. 03 - Onwards to Tensorflow JS!/003 Tensor Shape and Dimension.mp470.3MB
  37. 03 - Onwards to Tensorflow JS!/004 Elementwise Operations.mp443.18MB
  38. 03 - Onwards to Tensorflow JS!/005 Broadcasting Operations.mp424.22MB
  39. 03 - Onwards to Tensorflow JS!/006 Logging Tensor Data.mp410.68MB
  40. 03 - Onwards to Tensorflow JS!/007 Tensor Accessors.mp410.92MB
  41. 03 - Onwards to Tensorflow JS!/008 Creating Slices of Data.mp427.97MB
  42. 03 - Onwards to Tensorflow JS!/009 Tensor Concatenation.mp427.84MB
  43. 03 - Onwards to Tensorflow JS!/010 Summing Values Along an Axis.mp430.04MB
  44. 03 - Onwards to Tensorflow JS!/011 Massaging Dimensions with ExpandDims.mp427.21MB
  45. 04 - Applications of Tensorflow/001 KNN with Regression.mp419.02MB
  46. 04 - Applications of Tensorflow/002 A Change in Data Structure.mp415.11MB
  47. 04 - Applications of Tensorflow/003 KNN with Tensorflow.mp459.55MB
  48. 04 - Applications of Tensorflow/004 Maintaining Order Relationships.mp436.11MB
  49. 04 - Applications of Tensorflow/005 Sorting Tensors.mp429.49MB
  50. 04 - Applications of Tensorflow/006 Averaging Top Values.mp447.53MB
  51. 04 - Applications of Tensorflow/007 Moving to the Editor.mp426.79MB
  52. 04 - Applications of Tensorflow/008 Loading CSV Data.mp468.81MB
  53. 04 - Applications of Tensorflow/009 Running an Analysis.mp420.82MB
  54. 04 - Applications of Tensorflow/010 Reporting Error Percentages.mp440.68MB
  55. 04 - Applications of Tensorflow/011 Normalization or Standardization.mp477.88MB
  56. 04 - Applications of Tensorflow/012 Numerical Standardization with Tensorflow.mp440.17MB
  57. 04 - Applications of Tensorflow/013 Applying Standardization.mp436.51MB
  58. 04 - Applications of Tensorflow/014 Debugging Calculations.mp475.03MB
  59. 04 - Applications of Tensorflow/015 What Now.mp417.76MB
  60. 05 - Getting Started with Gradient Descent/001 Linear Regression.mp49.7MB
  61. 05 - Getting Started with Gradient Descent/002 Why Linear Regression.mp429.91MB
  62. 05 - Getting Started with Gradient Descent/003 Understanding Gradient Descent.mp477.88MB
  63. 05 - Getting Started with Gradient Descent/004 Guessing Coefficients with MSE.mp470.81MB
  64. 05 - Getting Started with Gradient Descent/005 Observations Around MSE.mp421.44MB
  65. 05 - Getting Started with Gradient Descent/006 Derivatives!.mp420.72MB
  66. 05 - Getting Started with Gradient Descent/007 Gradient Descent in Action.mp489.2MB
  67. 05 - Getting Started with Gradient Descent/008 Quick Breather and Review.mp439.05MB
  68. 05 - Getting Started with Gradient Descent/009 Why a Learning Rate.mp4148.47MB
  69. 05 - Getting Started with Gradient Descent/010 Answering Common Questions.mp429.84MB
  70. 05 - Getting Started with Gradient Descent/011 Gradient Descent with Multiple Terms.mp416.76MB
  71. 05 - Getting Started with Gradient Descent/012 Multiple Terms in Action.mp4103.27MB
  72. 06 - Gradient Descent with Tensorflow/001 Project Overview.mp424.94MB
  73. 06 - Gradient Descent with Tensorflow/002 Data Loading.mp419.56MB
  74. 06 - Gradient Descent with Tensorflow/003 Default Algorithm Options.mp426.6MB
  75. 06 - Gradient Descent with Tensorflow/004 Formulating the Training Loop.mp48.7MB
  76. 06 - Gradient Descent with Tensorflow/005 Initial Gradient Descent Implementation.mp467.66MB
  77. 06 - Gradient Descent with Tensorflow/006 Calculating MSE Slopes.mp451.06MB
  78. 06 - Gradient Descent with Tensorflow/007 Updating Coefficients.mp426.02MB
  79. 06 - Gradient Descent with Tensorflow/008 Interpreting Results.mp489.66MB
  80. 06 - Gradient Descent with Tensorflow/009 Matrix Multiplication.mp430.58MB
  81. 06 - Gradient Descent with Tensorflow/010 More on Matrix Multiplication.mp443.58MB
  82. 06 - Gradient Descent with Tensorflow/011 Matrix Form of Slope Equations.mp422.66MB
  83. 06 - Gradient Descent with Tensorflow/012 Simplification with Matrix Multiplication.mp460.76MB
  84. 06 - Gradient Descent with Tensorflow/013 How it All Works Together!.mp4110.57MB
  85. 07 - Increasing Performance with Vectorized Solutions/001 Refactoring the Linear Regression Class.mp445.89MB
  86. 07 - Increasing Performance with Vectorized Solutions/002 Refactoring to One Equation.mp463.81MB
  87. 07 - Increasing Performance with Vectorized Solutions/003 A Few More Changes.mp458.55MB
  88. 07 - Increasing Performance with Vectorized Solutions/004 Same Results Or Not.mp412MB
  89. 07 - Increasing Performance with Vectorized Solutions/005 Calculating Model Accuracy.mp456.49MB
  90. 07 - Increasing Performance with Vectorized Solutions/006 Implementing Coefficient of Determination.mp447.48MB
  91. 07 - Increasing Performance with Vectorized Solutions/007 Dealing with Bad Accuracy.mp457.68MB
  92. 07 - Increasing Performance with Vectorized Solutions/008 Reminder on Standardization.mp418.83MB
  93. 07 - Increasing Performance with Vectorized Solutions/009 Data Processing in a Helper Method.mp423.5MB
  94. 07 - Increasing Performance with Vectorized Solutions/010 Reapplying Standardization.mp450.2MB
  95. 07 - Increasing Performance with Vectorized Solutions/011 Fixing Standardization Issues.mp440.99MB
  96. 07 - Increasing Performance with Vectorized Solutions/012 Massaging Learning Rates.mp422.77MB
  97. 07 - Increasing Performance with Vectorized Solutions/013 Moving Towards Multivariate Regression.mp484.99MB
  98. 07 - Increasing Performance with Vectorized Solutions/014 Refactoring for Multivariate Analysis.mp472.4MB
  99. 07 - Increasing Performance with Vectorized Solutions/015 Learning Rate Optimization.mp453.01MB
  100. 07 - Increasing Performance with Vectorized Solutions/016 Recording MSE History.mp432.67MB
  101. 07 - Increasing Performance with Vectorized Solutions/017 Updating Learning Rate.mp427.96MB
  102. 08 - Plotting Data with Javascript/001 Observing Changing Learning Rate and MSE.mp435.39MB
  103. 08 - Plotting Data with Javascript/002 Plotting MSE Values.mp439.65MB
  104. 08 - Plotting Data with Javascript/003 Plotting MSE History against B Values.mp436.13MB
  105. 09 - Gradient Descent Alterations/001 Batch and Stochastic Gradient Descent.mp463.66MB
  106. 09 - Gradient Descent Alterations/002 Refactoring Towards Batch Gradient Descent.mp423.6MB
  107. 09 - Gradient Descent Alterations/003 Determining Batch Size and Quantity.mp453.93MB
  108. 09 - Gradient Descent Alterations/004 Iterating Over Batches.mp426.41MB
  109. 09 - Gradient Descent Alterations/005 Evaluating Batch Gradient Descent Results.mp450.7MB
  110. 09 - Gradient Descent Alterations/006 Making Predictions with the Model.mp433.83MB
  111. 10 - Natural Binary Classification/001 Introducing Logistic Regression.mp48.95MB
  112. 10 - Natural Binary Classification/002 Logistic Regression in Action.mp417.76MB
  113. 10 - Natural Binary Classification/003 Bad Equation Fits.mp433.59MB
  114. 10 - Natural Binary Classification/004 The Sigmoid Equation.mp430.37MB
  115. 10 - Natural Binary Classification/005 Decision Boundaries.mp435.04MB
  116. 10 - Natural Binary Classification/006 Changes for Logistic Regression.mp43.39MB
  117. 10 - Natural Binary Classification/007 Project Setup for Logistic Regression.mp446.88MB
  118. 10 - Natural Binary Classification/009 Importing Vehicle Data.mp433.05MB
  119. 10 - Natural Binary Classification/010 Encoding Label Values.mp429.85MB
  120. 10 - Natural Binary Classification/011 Updating Linear Regression for Logistic Regression.mp454.31MB
  121. 10 - Natural Binary Classification/012 The Sigmoid Equation with Logistic Regression.mp423.29MB
  122. 10 - Natural Binary Classification/013 A Touch More Refactoring.mp478.65MB
  123. 10 - Natural Binary Classification/014 Gauging Classification Accuracy.mp422.32MB
  124. 10 - Natural Binary Classification/015 Implementing a Test Function.mp419.98MB
  125. 10 - Natural Binary Classification/016 Variable Decision Boundaries.mp458.41MB
  126. 10 - Natural Binary Classification/017 Mean Squared Error vs Cross Entropy.mp443.54MB
  127. 10 - Natural Binary Classification/018 Refactoring with Cross Entropy.mp438.21MB
  128. 10 - Natural Binary Classification/019 Finishing the Cost Refactor.mp442.11MB
  129. 10 - Natural Binary Classification/020 Plotting Changing Cost History.mp433.29MB
  130. 11 - Multi-Value Classification/001 Multinominal Logistic Regression.mp46.56MB
  131. 11 - Multi-Value Classification/002 A Smart Refactor to Multinominal Analysis.mp414.51MB
  132. 11 - Multi-Value Classification/003 A Smarter Refactor!.mp428.54MB
  133. 11 - Multi-Value Classification/004 A Single Instance Approach.mp474.37MB
  134. 11 - Multi-Value Classification/005 Refactoring to Multi-Column Weights.mp430.25MB
  135. 11 - Multi-Value Classification/006 A Problem to Test Multinominal Classification.mp428.84MB
  136. 11 - Multi-Value Classification/007 Classifying Continuous Values.mp419.67MB
  137. 11 - Multi-Value Classification/008 Training a Multinominal Model.mp441.17MB
  138. 11 - Multi-Value Classification/009 Marginal vs Conditional Probability.mp468.35MB
  139. 11 - Multi-Value Classification/010 Sigmoid vs Softmax.mp444.6MB
  140. 11 - Multi-Value Classification/011 Refactoring Sigmoid to Softmax.mp441.8MB
  141. 11 - Multi-Value Classification/012 Implementing Accuracy Gauges.mp421.99MB
  142. 11 - Multi-Value Classification/013 Calculating Accuracy.mp411.66MB
  143. 12 - Image Recognition In Action/001 Handwriting Recognition.mp48.35MB
  144. 12 - Image Recognition In Action/002 Greyscale Values.mp438.45MB
  145. 12 - Image Recognition In Action/003 Many Features.mp436.72MB
  146. 12 - Image Recognition In Action/004 Flattening Image Data.mp436.99MB
  147. 12 - Image Recognition In Action/005 Encoding Label Values.mp438.26MB
  148. 12 - Image Recognition In Action/006 Implementing an Accuracy Gauge.mp462.17MB
  149. 12 - Image Recognition In Action/007 Unchanging Accuracy.mp47.03MB
  150. 12 - Image Recognition In Action/008 Debugging the Calculation Process.mp477.59MB
  151. 12 - Image Recognition In Action/009 Dealing with Zero Variances.mp422.96MB
  152. 12 - Image Recognition In Action/010 Backfilling Variance.mp416.48MB
  153. 13 - Performance Optimization/001 Handing Large Datasets.mp418.46MB
  154. 13 - Performance Optimization/002 Minimizing Memory Usage.mp415.15MB
  155. 13 - Performance Optimization/003 Creating Memory Snapshots.mp421.77MB
  156. 13 - Performance Optimization/004 The Javascript Garbage Collector.mp422.56MB
  157. 13 - Performance Optimization/005 Shallow vs Retained Memory Usage.mp446.68MB
  158. 13 - Performance Optimization/006 Measuring Memory Usage.mp485.87MB
  159. 13 - Performance Optimization/007 Releasing References.mp431.88MB
  160. 13 - Performance Optimization/008 Measuring Footprint Reduction.mp426.87MB
  161. 13 - Performance Optimization/009 Optimization Tensorflow Memory Usage.mp411.44MB
  162. 13 - Performance Optimization/010 Tensorflow's Eager Memory Usage.mp420.13MB
  163. 13 - Performance Optimization/011 Cleaning up Tensors with Tidy.mp49.42MB
  164. 13 - Performance Optimization/012 Implementing TF Tidy.mp413.67MB
  165. 13 - Performance Optimization/013 Tidying the Training Loop.mp441.06MB
  166. 13 - Performance Optimization/014 Measuring Reduced Memory Usage.mp411MB
  167. 13 - Performance Optimization/015 One More Optimization.mp421.45MB
  168. 13 - Performance Optimization/016 Final Memory Report.mp421.11MB
  169. 13 - Performance Optimization/017 Plotting Cost History.mp443.33MB
  170. 13 - Performance Optimization/018 NaN in Cost History.mp435.86MB
  171. 13 - Performance Optimization/019 Fixing Cost History.mp430.58MB
  172. 13 - Performance Optimization/020 Massaging Learning Parameters.mp413.55MB
  173. 13 - Performance Optimization/021 Improving Model Accuracy.mp433.65MB
  174. 14 - Appendix Custom CSV Loader/001 Loading CSV Files.mp45.98MB
  175. 14 - Appendix Custom CSV Loader/002 A Test Dataset.mp43.71MB
  176. 14 - Appendix Custom CSV Loader/003 Reading Files from Disk.mp46.6MB
  177. 14 - Appendix Custom CSV Loader/004 Splitting into Columns.mp46.7MB
  178. 14 - Appendix Custom CSV Loader/005 Dropping Trailing Columns.mp47.67MB
  179. 14 - Appendix Custom CSV Loader/006 Parsing Number Values.mp424.02MB
  180. 14 - Appendix Custom CSV Loader/007 Custom Value Parsing.mp414.02MB
  181. 14 - Appendix Custom CSV Loader/008 Extracting Data Columns.mp450.66MB
  182. 14 - Appendix Custom CSV Loader/009 Shuffling Data via Seed Phrase.mp432.89MB
  183. 14 - Appendix Custom CSV Loader/010 Splitting Test and Training.mp448.31MB
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