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[] Udemy - Machine Learning with Javascript

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  • 文件大小:7GB
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  • 最近下载:2023-11-28 09:46:37
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文件列表

  1. 05 - Getting Started with Gradient Descent/009 Why a Learning Rate.mp4 148MB
  2. 02 - Algorithm Overview/013 Investigating Optimal K Values.mp4 112MB
  3. 06 - Gradient Descent with Tensorflow/013 How it All Works Together!.mp4 111MB
  4. 05 - Getting Started with Gradient Descent/012 Multiple Terms in Action.mp4 103MB
  5. 06 - Gradient Descent with Tensorflow/008 Interpreting Results.mp4 90MB
  6. 05 - Getting Started with Gradient Descent/007 Gradient Descent in Action.mp4 89MB
  7. 13 - Performance Optimization/006 Measuring Memory Usage.mp4 86MB
  8. 07 - Increasing Performance with Vectorized Solutions/013 Moving Towards Multivariate Regression.mp4 85MB
  9. 10 - Natural Binary Classification/013 A Touch More Refactoring.mp4 79MB
  10. 04 - Applications of Tensorflow/011 Normalization or Standardization.mp4 78MB
  11. 05 - Getting Started with Gradient Descent/003 Understanding Gradient Descent.mp4 78MB
  12. 12 - Image Recognition In Action/008 Debugging the Calculation Process.mp4 78MB
  13. 04 - Applications of Tensorflow/014 Debugging Calculations.mp4 75MB
  14. 11 - Multi-Value Classification/004 A Single Instance Approach.mp4 74MB
  15. 07 - Increasing Performance with Vectorized Solutions/014 Refactoring for Multivariate Analysis.mp4 72MB
  16. 05 - Getting Started with Gradient Descent/004 Guessing Coefficients with MSE.mp4 71MB
  17. 03 - Onwards to Tensorflow JS!/003 Tensor Shape and Dimension.mp4 70MB
  18. 02 - Algorithm Overview/001 How K-Nearest Neighbor Works.mp4 70MB
  19. 04 - Applications of Tensorflow/008 Loading CSV Data.mp4 69MB
  20. 11 - Multi-Value Classification/009 Marginal vs Conditional Probability.mp4 68MB
  21. 06 - Gradient Descent with Tensorflow/005 Initial Gradient Descent Implementation.mp4 68MB
  22. 07 - Increasing Performance with Vectorized Solutions/002 Refactoring to One Equation.mp4 64MB
  23. 09 - Gradient Descent Alterations/001 Batch and Stochastic Gradient Descent.mp4 64MB
  24. 01 - What is Machine Learning/005 A Complete Walkthrough.mp4 63MB
  25. 01 - What is Machine Learning/004 Solving Machine Learning Problems.mp4 63MB
  26. 12 - Image Recognition In Action/006 Implementing an Accuracy Gauge.mp4 62MB
  27. 06 - Gradient Descent with Tensorflow/012 Simplification with Matrix Multiplication.mp4 61MB
  28. 04 - Applications of Tensorflow/003 KNN with Tensorflow.mp4 60MB
  29. 02 - Algorithm Overview/016 N-Dimension Distance.mp4 59MB
  30. 02 - Algorithm Overview/003 Implementing KNN.mp4 59MB
  31. 07 - Increasing Performance with Vectorized Solutions/003 A Few More Changes.mp4 59MB
  32. 10 - Natural Binary Classification/016 Variable Decision Boundaries.mp4 58MB
  33. 02 - Algorithm Overview/017 Arbitrary Feature Spaces.mp4 58MB
  34. 07 - Increasing Performance with Vectorized Solutions/007 Dealing with Bad Accuracy.mp4 58MB
  35. 02 - Algorithm Overview/022 Feature Selection with KNN.mp4 57MB
  36. 07 - Increasing Performance with Vectorized Solutions/005 Calculating Model Accuracy.mp4 56MB
  37. 02 - Algorithm Overview/020 Normalization with MinMax.mp4 54MB
  38. 10 - Natural Binary Classification/011 Updating Linear Regression for Logistic Regression.mp4 54MB
  39. 02 - Algorithm Overview/019 Feature Normalization.mp4 54MB
  40. 09 - Gradient Descent Alterations/003 Determining Batch Size and Quantity.mp4 54MB
  41. 07 - Increasing Performance with Vectorized Solutions/015 Learning Rate Optimization.mp4 53MB
  42. 06 - Gradient Descent with Tensorflow/006 Calculating MSE Slopes.mp4 51MB
  43. 09 - Gradient Descent Alterations/005 Evaluating Batch Gradient Descent Results.mp4 51MB
  44. 14 - Appendix Custom CSV Loader/008 Extracting Data Columns.mp4 51MB
  45. 07 - Increasing Performance with Vectorized Solutions/010 Reapplying Standardization.mp4 50MB
  46. 02 - Algorithm Overview/014 Updating KNN for Multiple Features.mp4 49MB
  47. 14 - Appendix Custom CSV Loader/010 Splitting Test and Training.mp4 48MB
  48. 02 - Algorithm Overview/002 Lodash Review.mp4 48MB
  49. 01 - What is Machine Learning/009 Dataset Structures.mp4 48MB
  50. 04 - Applications of Tensorflow/006 Averaging Top Values.mp4 48MB
  51. 07 - Increasing Performance with Vectorized Solutions/006 Implementing Coefficient of Determination.mp4 47MB
  52. 10 - Natural Binary Classification/007 Project Setup for Logistic Regression.mp4 47MB
  53. 03 - Onwards to Tensorflow JS!/001 Let's Get Our Bearings.mp4 47MB
  54. 13 - Performance Optimization/005 Shallow vs Retained Memory Usage.mp4 47MB
  55. 02 - Algorithm Overview/018 Magnitude Offsets in Features.mp4 46MB
  56. 02 - Algorithm Overview/010 Gauging Accuracy.mp4 46MB
  57. 07 - Increasing Performance with Vectorized Solutions/001 Refactoring the Linear Regression Class.mp4 46MB
  58. 02 - Algorithm Overview/005 Testing the Algorithm.mp4 45MB
  59. 11 - Multi-Value Classification/010 Sigmoid vs Softmax.mp4 45MB
  60. 06 - Gradient Descent with Tensorflow/010 More on Matrix Multiplication.mp4 44MB
  61. 10 - Natural Binary Classification/017 Mean Squared Error vs Cross Entropy.mp4 44MB
  62. 13 - Performance Optimization/017 Plotting Cost History.mp4 43MB
  63. 03 - Onwards to Tensorflow JS!/004 Elementwise Operations.mp4 43MB
  64. 10 - Natural Binary Classification/019 Finishing the Cost Refactor.mp4 42MB
  65. 11 - Multi-Value Classification/011 Refactoring Sigmoid to Softmax.mp4 42MB
  66. 11 - Multi-Value Classification/008 Training a Multinominal Model.mp4 41MB
  67. 13 - Performance Optimization/013 Tidying the Training Loop.mp4 41MB
  68. 07 - Increasing Performance with Vectorized Solutions/011 Fixing Standardization Issues.mp4 41MB
  69. 04 - Applications of Tensorflow/010 Reporting Error Percentages.mp4 41MB
  70. 04 - Applications of Tensorflow/012 Numerical Standardization with Tensorflow.mp4 40MB
  71. 08 - Plotting Data with Javascript/002 Plotting MSE Values.mp4 40MB
  72. 05 - Getting Started with Gradient Descent/008 Quick Breather and Review.mp4 39MB
  73. 02 - Algorithm Overview/009 Generalizing KNN.mp4 39MB
  74. 02 - Algorithm Overview/021 Applying Normalization.mp4 39MB
  75. 12 - Image Recognition In Action/002 Greyscale Values.mp4 38MB
  76. 12 - Image Recognition In Action/005 Encoding Label Values.mp4 38MB
  77. 10 - Natural Binary Classification/018 Refactoring with Cross Entropy.mp4 38MB
  78. 02 - Algorithm Overview/004 Finishing KNN Implementation.mp4 38MB
  79. 12 - Image Recognition In Action/004 Flattening Image Data.mp4 37MB
  80. 03 - Onwards to Tensorflow JS!/002 A Plan to Move Forward.mp4 37MB
  81. 12 - Image Recognition In Action/003 Many Features.mp4 37MB
  82. 04 - Applications of Tensorflow/013 Applying Standardization.mp4 37MB
  83. 08 - Plotting Data with Javascript/003 Plotting MSE History against B Values.mp4 36MB
  84. 04 - Applications of Tensorflow/004 Maintaining Order Relationships.mp4 36MB
  85. 13 - Performance Optimization/018 NaN in Cost History.mp4 36MB
  86. 08 - Plotting Data with Javascript/001 Observing Changing Learning Rate and MSE.mp4 35MB
  87. 10 - Natural Binary Classification/005 Decision Boundaries.mp4 35MB
  88. 01 - What is Machine Learning/008 Identifying Relevant Data.mp4 34MB
  89. 09 - Gradient Descent Alterations/006 Making Predictions with the Model.mp4 34MB
  90. 02 - Algorithm Overview/012 Refactoring Accuracy Reporting.mp4 34MB
  91. 13 - Performance Optimization/021 Improving Model Accuracy.mp4 34MB
  92. 10 - Natural Binary Classification/003 Bad Equation Fits.mp4 34MB
  93. 10 - Natural Binary Classification/020 Plotting Changing Cost History.mp4 33MB
  94. 02 - Algorithm Overview/011 Printing a Report.mp4 33MB
  95. 10 - Natural Binary Classification/009 Importing Vehicle Data.mp4 33MB
  96. 14 - Appendix Custom CSV Loader/009 Shuffling Data via Seed Phrase.mp4 33MB
  97. 07 - Increasing Performance with Vectorized Solutions/016 Recording MSE History.mp4 33MB
  98. 02 - Algorithm Overview/015 Multi-Dimensional KNN.mp4 32MB
  99. 13 - Performance Optimization/007 Releasing References.mp4 32MB
  100. 06 - Gradient Descent with Tensorflow/009 Matrix Multiplication.mp4 31MB
  101. 13 - Performance Optimization/019 Fixing Cost History.mp4 31MB
  102. 10 - Natural Binary Classification/004 The Sigmoid Equation.mp4 30MB
  103. 11 - Multi-Value Classification/005 Refactoring to Multi-Column Weights.mp4 30MB
  104. 03 - Onwards to Tensorflow JS!/010 Summing Values Along an Axis.mp4 30MB
  105. 05 - Getting Started with Gradient Descent/002 Why Linear Regression.mp4 30MB
  106. 10 - Natural Binary Classification/010 Encoding Label Values.mp4 30MB
  107. 05 - Getting Started with Gradient Descent/010 Answering Common Questions.mp4 30MB
  108. 04 - Applications of Tensorflow/005 Sorting Tensors.mp4 29MB
  109. 11 - Multi-Value Classification/006 A Problem to Test Multinominal Classification.mp4 29MB
  110. 11 - Multi-Value Classification/003 A Smarter Refactor!.mp4 29MB
  111. 02 - Algorithm Overview/023 Objective Feature Picking.mp4 28MB
  112. 03 - Onwards to Tensorflow JS!/008 Creating Slices of Data.mp4 28MB
  113. 07 - Increasing Performance with Vectorized Solutions/017 Updating Learning Rate.mp4 28MB
  114. 03 - Onwards to Tensorflow JS!/009 Tensor Concatenation.mp4 28MB
  115. 02 - Algorithm Overview/007 Test and Training Data.mp4 27MB
  116. 03 - Onwards to Tensorflow JS!/011 Massaging Dimensions with ExpandDims.mp4 27MB
  117. 13 - Performance Optimization/008 Measuring Footprint Reduction.mp4 27MB
  118. 04 - Applications of Tensorflow/007 Moving to the Editor.mp4 27MB
  119. 06 - Gradient Descent with Tensorflow/003 Default Algorithm Options.mp4 27MB
  120. 09 - Gradient Descent Alterations/004 Iterating Over Batches.mp4 26MB
  121. 06 - Gradient Descent with Tensorflow/007 Updating Coefficients.mp4 26MB
  122. 02 - Algorithm Overview/006 Interpreting Bad Results.mp4 26MB
  123. 06 - Gradient Descent with Tensorflow/001 Project Overview.mp4 25MB
  124. 03 - Onwards to Tensorflow JS!/005 Broadcasting Operations.mp4 24MB
  125. 14 - Appendix Custom CSV Loader/006 Parsing Number Values.mp4 24MB
  126. 09 - Gradient Descent Alterations/002 Refactoring Towards Batch Gradient Descent.mp4 24MB
  127. 07 - Increasing Performance with Vectorized Solutions/009 Data Processing in a Helper Method.mp4 24MB
  128. 10 - Natural Binary Classification/012 The Sigmoid Equation with Logistic Regression.mp4 23MB
  129. 12 - Image Recognition In Action/009 Dealing with Zero Variances.mp4 23MB
  130. 01 - What is Machine Learning/007 Problem Outline.mp4 23MB
  131. 07 - Increasing Performance with Vectorized Solutions/012 Massaging Learning Rates.mp4 23MB
  132. 06 - Gradient Descent with Tensorflow/011 Matrix Form of Slope Equations.mp4 23MB
  133. 13 - Performance Optimization/004 The Javascript Garbage Collector.mp4 23MB
  134. 10 - Natural Binary Classification/014 Gauging Classification Accuracy.mp4 22MB
  135. 11 - Multi-Value Classification/012 Implementing Accuracy Gauges.mp4 22MB
  136. 13 - Performance Optimization/003 Creating Memory Snapshots.mp4 22MB
  137. 13 - Performance Optimization/015 One More Optimization.mp4 21MB
  138. 05 - Getting Started with Gradient Descent/005 Observations Around MSE.mp4 21MB
  139. 13 - Performance Optimization/016 Final Memory Report.mp4 21MB
  140. 02 - Algorithm Overview/024 Evaluating Different Feature Values.mp4 21MB
  141. 04 - Applications of Tensorflow/009 Running an Analysis.mp4 21MB
  142. 05 - Getting Started with Gradient Descent/006 Derivatives!.mp4 21MB
  143. 13 - Performance Optimization/010 Tensorflow's Eager Memory Usage.mp4 20MB
  144. 10 - Natural Binary Classification/015 Implementing a Test Function.mp4 20MB
  145. 11 - Multi-Value Classification/007 Classifying Continuous Values.mp4 20MB
  146. 06 - Gradient Descent with Tensorflow/002 Data Loading.mp4 20MB
  147. 04 - Applications of Tensorflow/001 KNN with Regression.mp4 19MB
  148. 07 - Increasing Performance with Vectorized Solutions/008 Reminder on Standardization.mp4 19MB
  149. 13 - Performance Optimization/001 Handing Large Datasets.mp4 18MB
  150. 04 - Applications of Tensorflow/015 What Now.mp4 18MB
  151. 10 - Natural Binary Classification/002 Logistic Regression in Action.mp4 18MB
  152. 01 - What is Machine Learning/011 What Type of Problem.mp4 17MB
  153. 05 - Getting Started with Gradient Descent/011 Gradient Descent with Multiple Terms.mp4 17MB
  154. 12 - Image Recognition In Action/010 Backfilling Variance.mp4 16MB
  155. 13 - Performance Optimization/002 Minimizing Memory Usage.mp4 15MB
  156. 04 - Applications of Tensorflow/002 A Change in Data Structure.mp4 15MB
  157. 11 - Multi-Value Classification/002 A Smart Refactor to Multinominal Analysis.mp4 15MB
  158. 14 - Appendix Custom CSV Loader/007 Custom Value Parsing.mp4 14MB
  159. 13 - Performance Optimization/012 Implementing TF Tidy.mp4 14MB
  160. 13 - Performance Optimization/020 Massaging Learning Parameters.mp4 14MB
  161. 02 - Algorithm Overview/008 Randomizing Test Data.mp4 13MB
  162. 01 - What is Machine Learning/010 Recording Observation Data.mp4 13MB
  163. 07 - Increasing Performance with Vectorized Solutions/004 Same Results Or Not.mp4 12MB
  164. 11 - Multi-Value Classification/013 Calculating Accuracy.mp4 12MB
  165. 13 - Performance Optimization/009 Optimization Tensorflow Memory Usage.mp4 11MB
  166. 13 - Performance Optimization/014 Measuring Reduced Memory Usage.mp4 11MB
  167. 03 - Onwards to Tensorflow JS!/007 Tensor Accessors.mp4 11MB
  168. 03 - Onwards to Tensorflow JS!/006 Logging Tensor Data.mp4 11MB
  169. 05 - Getting Started with Gradient Descent/001 Linear Regression.mp4 10MB
  170. 13 - Performance Optimization/011 Cleaning up Tensors with Tidy.mp4 9MB
  171. 10 - Natural Binary Classification/001 Introducing Logistic Regression.mp4 9MB
  172. 06 - Gradient Descent with Tensorflow/004 Formulating the Training Loop.mp4 9MB
  173. 12 - Image Recognition In Action/001 Handwriting Recognition.mp4 8MB
  174. 01 - What is Machine Learning/001 Getting Started - How to Get Help.mp4 8MB
  175. 01 - What is Machine Learning/006 App Setup.mp4 8MB
  176. 14 - Appendix Custom CSV Loader/005 Dropping Trailing Columns.mp4 8MB
  177. 12 - Image Recognition In Action/007 Unchanging Accuracy.mp4 7MB
  178. 14 - Appendix Custom CSV Loader/004 Splitting into Columns.mp4 7MB
  179. 14 - Appendix Custom CSV Loader/003 Reading Files from Disk.mp4 7MB
  180. 11 - Multi-Value Classification/001 Multinominal Logistic Regression.mp4 7MB
  181. 14 - Appendix Custom CSV Loader/001 Loading CSV Files.mp4 6MB
  182. 14 - Appendix Custom CSV Loader/002 A Test Dataset.mp4 4MB
  183. 10 - Natural Binary Classification/006 Changes for Logistic Regression.mp4 3MB
  184. 01 - What is Machine Learning/002 diagrams.zip 790KB
  185. 10 - Natural Binary Classification/008 regressions.zip 34KB
  186. 05 - Getting Started with Gradient Descent/009 Why a Learning Rate_en.srt 27KB
  187. 06 - Gradient Descent with Tensorflow/013 How it All Works Together!_en.srt 22KB
  188. 05 - Getting Started with Gradient Descent/003 Understanding Gradient Descent_en.srt 20KB
  189. 03 - Onwards to Tensorflow JS!/003 Tensor Shape and Dimension_en.srt 20KB
  190. 07 - Increasing Performance with Vectorized Solutions/013 Moving Towards Multivariate Regression_en.srt 19KB
  191. 05 - Getting Started with Gradient Descent/007 Gradient Descent in Action_en.srt 19KB
  192. 05 - Getting Started with Gradient Descent/012 Multiple Terms in Action_en.srt 17KB
  193. 11 - Multi-Value Classification/009 Marginal vs Conditional Probability_en.srt 16KB
  194. 02 - Algorithm Overview/016 N-Dimension Distance_en.srt 16KB
  195. 11 - Multi-Value Classification/004 A Single Instance Approach_en.srt 16KB
  196. 05 - Getting Started with Gradient Descent/004 Guessing Coefficients with MSE_en.srt 16KB
  197. 06 - Gradient Descent with Tensorflow/008 Interpreting Results_en.srt 16KB
  198. 04 - Applications of Tensorflow/008 Loading CSV Data_en.srt 16KB
  199. 01 - What is Machine Learning/005 A Complete Walkthrough_en.srt 15KB
  200. 02 - Algorithm Overview/002 Lodash Review_en.srt 15KB
  201. 06 - Gradient Descent with Tensorflow/012 Simplification with Matrix Multiplication_en.srt 15KB
  202. 04 - Applications of Tensorflow/003 KNN with Tensorflow_en.srt 15KB
  203. 06 - Gradient Descent with Tensorflow/005 Initial Gradient Descent Implementation_en.srt 14KB
  204. 07 - Increasing Performance with Vectorized Solutions/002 Refactoring to One Equation_en.srt 14KB
  205. 13 - Performance Optimization/006 Measuring Memory Usage_en.srt 14KB
  206. 07 - Increasing Performance with Vectorized Solutions/005 Calculating Model Accuracy_en.srt 14KB
  207. 02 - Algorithm Overview/017 Arbitrary Feature Spaces_en.srt 14KB
  208. 02 - Algorithm Overview/001 How K-Nearest Neighbor Works_en.srt 14KB
  209. 06 - Gradient Descent with Tensorflow/003 Default Algorithm Options_en.srt 13KB
  210. 02 - Algorithm Overview/022 Feature Selection with KNN_en.srt 13KB
  211. 12 - Image Recognition In Action/008 Debugging the Calculation Process_en.srt 13KB
  212. 07 - Increasing Performance with Vectorized Solutions/015 Learning Rate Optimization_en.srt 13KB
  213. 09 - Gradient Descent Alterations/004 Iterating Over Batches_en.srt 13KB
  214. 04 - Applications of Tensorflow/005 Sorting Tensors_en.srt 13KB
  215. 10 - Natural Binary Classification/005 Decision Boundaries_en.srt 13KB
  216. 09 - Gradient Descent Alterations/006 Making Predictions with the Model_en.srt 12KB
  217. 03 - Onwards to Tensorflow JS!/011 Massaging Dimensions with ExpandDims_en.srt 12KB
  218. 14 - Appendix Custom CSV Loader/010 Splitting Test and Training_en.srt 12KB
  219. 03 - Onwards to Tensorflow JS!/004 Elementwise Operations_en.srt 12KB
  220. 03 - Onwards to Tensorflow JS!/001 Let's Get Our Bearings_en.srt 12KB
  221. 07 - Increasing Performance with Vectorized Solutions/007 Dealing with Bad Accuracy_en.srt 12KB
  222. 07 - Increasing Performance with Vectorized Solutions/014 Refactoring for Multivariate Analysis_en.srt 12KB
  223. 07 - Increasing Performance with Vectorized Solutions/001 Refactoring the Linear Regression Class_en.srt 12KB
  224. 02 - Algorithm Overview/019 Feature Normalization_en.srt 12KB
  225. 04 - Applications of Tensorflow/012 Numerical Standardization with Tensorflow_en.srt 12KB
  226. 10 - Natural Binary Classification/013 A Touch More Refactoring_en.srt 12KB
  227. 04 - Applications of Tensorflow/011 Normalization or Standardization_en.srt 12KB
  228. 03 - Onwards to Tensorflow JS!/008 Creating Slices of Data_en.srt 12KB
  229. 07 - Increasing Performance with Vectorized Solutions/006 Implementing Coefficient of Determination_en.srt 12KB
  230. 09 - Gradient Descent Alterations/001 Batch and Stochastic Gradient Descent_en.srt 11KB
  231. 06 - Gradient Descent with Tensorflow/009 Matrix Multiplication_en.srt 11KB
  232. 05 - Getting Started with Gradient Descent/006 Derivatives!_en.srt 11KB
  233. 10 - Natural Binary Classification/002 Logistic Regression in Action_en.srt 11KB
  234. 03 - Onwards to Tensorflow JS!/005 Broadcasting Operations_en.srt 11KB
  235. 02 - Algorithm Overview/020 Normalization with MinMax_en.srt 11KB
  236. 04 - Applications of Tensorflow/004 Maintaining Order Relationships_en.srt 11KB
  237. 02 - Algorithm Overview/014 Updating KNN for Multiple Features_en.srt 11KB
  238. 02 - Algorithm Overview/003 Implementing KNN_en.srt 10KB
  239. 07 - Increasing Performance with Vectorized Solutions/017 Updating Learning Rate_en.srt 10KB
  240. 11 - Multi-Value Classification/010 Sigmoid vs Softmax_en.srt 10KB
  241. 13 - Performance Optimization/004 The Javascript Garbage Collector_en.srt 10KB
  242. 07 - Increasing Performance with Vectorized Solutions/003 A Few More Changes_en.srt 10KB
  243. 12 - Image Recognition In Action/009 Dealing with Zero Variances_en.srt 10KB
  244. 04 - Applications of Tensorflow/010 Reporting Error Percentages_en.srt 10KB
  245. 06 - Gradient Descent with Tensorflow/006 Calculating MSE Slopes_en.srt 10KB
  246. 06 - Gradient Descent with Tensorflow/011 Matrix Form of Slope Equations_en.srt 10KB
  247. 01 - What is Machine Learning/004 Solving Machine Learning Problems_en.srt 10KB
  248. 06 - Gradient Descent with Tensorflow/001 Project Overview_en.srt 9KB
  249. 12 - Image Recognition In Action/005 Encoding Label Values_en.srt 9KB
  250. 09 - Gradient Descent Alterations/005 Evaluating Batch Gradient Descent Results_en.srt 9KB
  251. 13 - Performance Optimization/005 Shallow vs Retained Memory Usage_en.srt 9KB
  252. 05 - Getting Started with Gradient Descent/005 Observations Around MSE_en.srt 9KB
  253. 01 - What is Machine Learning/009 Dataset Structures_en.srt 9KB
  254. 09 - Gradient Descent Alterations/003 Determining Batch Size and Quantity_en.srt 9KB
  255. 10 - Natural Binary Classification/007 Project Setup for Logistic Regression_en.srt 9KB
  256. 07 - Increasing Performance with Vectorized Solutions/011 Fixing Standardization Issues_en.srt 9KB
  257. 10 - Natural Binary Classification/017 Mean Squared Error vs Cross Entropy_en.srt 9KB
  258. 02 - Algorithm Overview/018 Magnitude Offsets in Features_en.srt 9KB
  259. 03 - Onwards to Tensorflow JS!/007 Tensor Accessors_en.srt 9KB
  260. 02 - Algorithm Overview/004 Finishing KNN Implementation_en.srt 9KB
  261. 14 - Appendix Custom CSV Loader/009 Shuffling Data via Seed Phrase_en.srt 9KB
  262. 10 - Natural Binary Classification/003 Bad Equation Fits_en.srt 9KB
  263. 10 - Natural Binary Classification/015 Implementing a Test Function_en.srt 9KB
  264. 03 - Onwards to Tensorflow JS!/009 Tensor Concatenation_en.srt 9KB
  265. 07 - Increasing Performance with Vectorized Solutions/010 Reapplying Standardization_en.srt 9KB
  266. 11 - Multi-Value Classification/002 A Smart Refactor to Multinominal Analysis_en.srt 8KB
  267. 08 - Plotting Data with Javascript/002 Plotting MSE Values_en.srt 8KB
  268. 04 - Applications of Tensorflow/001 KNN with Regression_en.srt 8KB
  269. 03 - Onwards to Tensorflow JS!/010 Summing Values Along an Axis_en.srt 8KB
  270. 13 - Performance Optimization/003 Creating Memory Snapshots_en.srt 8KB
  271. 07 - Increasing Performance with Vectorized Solutions/016 Recording MSE History_en.srt 8KB
  272. 14 - Appendix Custom CSV Loader/008 Extracting Data Columns_en.srt 8KB
  273. 02 - Algorithm Overview/010 Gauging Accuracy_en.srt 8KB
  274. 12 - Image Recognition In Action/002 Greyscale Values_en.srt 8KB
  275. 01 - What is Machine Learning/011 What Type of Problem_en.srt 8KB
  276. 05 - Getting Started with Gradient Descent/002 Why Linear Regression_en.srt 8KB
  277. 02 - Algorithm Overview/012 Refactoring Accuracy Reporting_en.srt 8KB
  278. 11 - Multi-Value Classification/011 Refactoring Sigmoid to Softmax_en.srt 8KB
  279. 06 - Gradient Descent with Tensorflow/002 Data Loading_en.srt 8KB
  280. 13 - Performance Optimization/019 Fixing Cost History_en.srt 8KB
  281. 03 - Onwards to Tensorflow JS!/002 A Plan to Move Forward_en.srt 8KB
  282. 13 - Performance Optimization/002 Minimizing Memory Usage_en.srt 8KB
  283. 11 - Multi-Value Classification/005 Refactoring to Multi-Column Weights_en.srt 7KB
  284. 05 - Getting Started with Gradient Descent/011 Gradient Descent with Multiple Terms_en.srt 7KB
  285. 02 - Algorithm Overview/005 Testing the Algorithm_en.srt 7KB
  286. 13 - Performance Optimization/010 Tensorflow's Eager Memory Usage_en.srt 7KB
  287. 10 - Natural Binary Classification/004 The Sigmoid Equation_en.srt 7KB
  288. 11 - Multi-Value Classification/006 A Problem to Test Multinominal Classification_en.srt 7KB
  289. 13 - Performance Optimization/001 Handing Large Datasets_en.srt 7KB
  290. 10 - Natural Binary Classification/012 The Sigmoid Equation with Logistic Regression_en.srt 7KB
  291. 07 - Increasing Performance with Vectorized Solutions/008 Reminder on Standardization_en.srt 7KB
  292. 02 - Algorithm Overview/021 Applying Normalization_en.srt 7KB
  293. 13 - Performance Optimization/018 NaN in Cost History_en.srt 7KB
  294. 01 - What is Machine Learning/008 Identifying Relevant Data_en.srt 7KB
  295. 08 - Plotting Data with Javascript/001 Observing Changing Learning Rate and MSE_en.srt 7KB
  296. 13 - Performance Optimization/017 Plotting Cost History_en.srt 7KB
  297. 13 - Performance Optimization/021 Improving Model Accuracy_en.srt 7KB
  298. 10 - Natural Binary Classification/019 Finishing the Cost Refactor_en.srt 7KB
  299. 03 - Onwards to Tensorflow JS!/006 Logging Tensor Data_en.srt 7KB
  300. 04 - Applications of Tensorflow/013 Applying Standardization_en.srt 7KB
  301. 14 - Appendix Custom CSV Loader/007 Custom Value Parsing_en.srt 7KB
  302. 02 - Algorithm Overview/006 Interpreting Bad Results_en.srt 7KB
  303. 04 - Applications of Tensorflow/002 A Change in Data Structure_en.srt 7KB
  304. 02 - Algorithm Overview/015 Multi-Dimensional KNN_en.srt 6KB
  305. 13 - Performance Optimization/008 Measuring Footprint Reduction_en.srt 6KB
  306. 04 - Applications of Tensorflow/015 What Now_en.srt 6KB
  307. 02 - Algorithm Overview/007 Test and Training Data_en.srt 6KB
  308. 13 - Performance Optimization/013 Tidying the Training Loop_en.srt 6KB
  309. 05 - Getting Started with Gradient Descent/010 Answering Common Questions_en.srt 6KB
  310. 11 - Multi-Value Classification/003 A Smarter Refactor!_en.srt 6KB
  311. 02 - Algorithm Overview/008 Randomizing Test Data_en.srt 6KB
  312. 02 - Algorithm Overview/009 Generalizing KNN_en.srt 6KB
  313. 07 - Increasing Performance with Vectorized Solutions/004 Same Results Or Not_en.srt 6KB
  314. 10 - Natural Binary Classification/020 Plotting Changing Cost History_en.srt 6KB
  315. 07 - Increasing Performance with Vectorized Solutions/009 Data Processing in a Helper Method_en.srt 6KB
  316. 10 - Natural Binary Classification/014 Gauging Classification Accuracy_en.srt 6KB
  317. 14 - Appendix Custom CSV Loader/006 Parsing Number Values_en.srt 5KB
  318. 12 - Image Recognition In Action/003 Many Features_en.srt 5KB
  319. 04 - Applications of Tensorflow/007 Moving to the Editor_en.srt 5KB
  320. 02 - Algorithm Overview/011 Printing a Report_en.srt 5KB
  321. 01 - What is Machine Learning/007 Problem Outline_en.srt 5KB
  322. 06 - Gradient Descent with Tensorflow/007 Updating Coefficients_en.srt 5KB
  323. 11 - Multi-Value Classification/013 Calculating Accuracy_en.srt 5KB
  324. 06 - Gradient Descent with Tensorflow/004 Formulating the Training Loop_en.srt 5KB
  325. 13 - Performance Optimization/007 Releasing References_en.srt 5KB
  326. 05 - Getting Started with Gradient Descent/001 Linear Regression_en.srt 5KB
  327. 02 - Algorithm Overview/024 Evaluating Different Feature Values_en.srt 4KB
  328. 13 - Performance Optimization/011 Cleaning up Tensors with Tidy_en.srt 4KB
  329. 12 - Image Recognition In Action/010 Backfilling Variance_en.srt 4KB
  330. 10 - Natural Binary Classification/001 Introducing Logistic Regression_en.srt 4KB
  331. 13 - Performance Optimization/015 One More Optimization_en.srt 4KB
  332. 14 - Appendix Custom CSV Loader/005 Dropping Trailing Columns_en.srt 4KB
  333. 11 - Multi-Value Classification/001 Multinominal Logistic Regression_en.srt 4KB
  334. 12 - Image Recognition In Action/001 Handwriting Recognition_en.srt 4KB
  335. 15 - Extras/001 Bonus!.html 4KB
  336. 14 - Appendix Custom CSV Loader/001 Loading CSV Files_en.srt 3KB
  337. 12 - Image Recognition In Action/007 Unchanging Accuracy_en.srt 3KB
  338. 14 - Appendix Custom CSV Loader/002 A Test Dataset_en.srt 3KB
  339. 13 - Performance Optimization/009 Optimization Tensorflow Memory Usage_en.srt 3KB
  340. 13 - Performance Optimization/020 Massaging Learning Parameters_en.srt 3KB
  341. 13 - Performance Optimization/014 Measuring Reduced Memory Usage_en.srt 2KB
  342. 10 - Natural Binary Classification/006 Changes for Logistic Regression_en.srt 2KB
  343. 01 - What is Machine Learning/001 Getting Started - How to Get Help_en.srt 2KB
  344. 01 - What is Machine Learning/002 Course Resources.html 1KB
  345. 01 - What is Machine Learning/003 Join Our Community!.html 318B
  346. 10 - Natural Binary Classification/008 Project Download.html 213B
  347. 0. Websites you may like/[FreeCourseSite.com].url 127B
  348. 02 - Algorithm Overview/0. Websites you may like/[FreeCourseSite.com].url 127B
  349. 09 - Gradient Descent Alterations/0. Websites you may like/[FreeCourseSite.com].url 127B
  350. 13 - Performance Optimization/0. Websites you may like/[FreeCourseSite.com].url 127B
  351. 0. Websites you may like/[CourseClub.Me].url 122B
  352. 02 - Algorithm Overview/0. Websites you may like/[CourseClub.Me].url 122B
  353. 09 - Gradient Descent Alterations/0. Websites you may like/[CourseClub.Me].url 122B
  354. 13 - Performance Optimization/0. Websites you may like/[CourseClub.Me].url 122B
  355. 0. Websites you may like/[GigaCourse.Com].url 49B
  356. 02 - Algorithm Overview/0. Websites you may like/[GigaCourse.Com].url 49B
  357. 09 - Gradient Descent Alterations/0. Websites you may like/[GigaCourse.Com].url 49B
  358. 13 - Performance Optimization/0. Websites you may like/[GigaCourse.Com].url 49B