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[] Udemy - Machine Learning with Javascript

  • 收录时间:2020-03-26 17:37:32
  • 文件大小:10GB
  • 下载次数:22
  • 最近下载:2021-01-14 21:05:15
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文件列表

  1. 05 Getting Started with Gradient Descent/068 Why a Learning Rate.mp4 187MB
  2. 06 Gradient Descent with Tensorflow/084 How it All Works Together.mp4 144MB
  3. 02 Algorithm Overview/022 Investigating Optimal K Values.mp4 129MB
  4. 05 Getting Started with Gradient Descent/062 Understanding Gradient Descent.mp4 127MB
  5. 05 Getting Started with Gradient Descent/071 Multiple Terms in Action.mp4 123MB
  6. 07 Increasing Performance with Vectorized Solutions/097 Moving Towards Multivariate Regression.mp4 121MB
  7. 05 Getting Started with Gradient Descent/066 Gradient Descent in Action.mp4 115MB
  8. 03 Onwards to Tensorflow JS/036 Tensor Shape and Dimension.mp4 114MB
  9. 01 What is Machine Learning/003 A Complete Walkthrough.mp4 109MB
  10. 11 Multi-Value Classification/134 A Single Instance Approach.mp4 104MB
  11. 06 Gradient Descent with Tensorflow/079 Interpreting Results.mp4 102MB
  12. 13 Performance Optimization/159 Measuring Memory Usage.mp4 97MB
  13. 11 Multi-Value Classification/139 Marginal vs Conditional Probability.mp4 95MB
  14. 05 Getting Started with Gradient Descent/063 Guessing Coefficients with MSE.mp4 93MB
  15. 02 Algorithm Overview/010 How K-Nearest Neighbor Works.mp4 93MB
  16. 04 Applications of Tensorflow/055 Normalization or Standardization.mp4 93MB
  17. 06 Gradient Descent with Tensorflow/083 Simplification with Matrix Multiplication.mp4 91MB
  18. 04 Applications of Tensorflow/052 Loading CSV Data.mp4 89MB
  19. 12 Image Recognition In Action/151 Debugging the Calculation Process.mp4 89MB
  20. 06 Gradient Descent with Tensorflow/076 Initial Gradient Descent Implementation.mp4 88MB
  21. 10 Natural Binary Classification/123 A Touch More Refactoring.mp4 87MB
  22. 04 Applications of Tensorflow/058 Debugging Calculations.mp4 87MB
  23. 07 Increasing Performance with Vectorized Solutions/086 Refactoring to One Equation.mp4 85MB
  24. 07 Increasing Performance with Vectorized Solutions/098 Refactoring for Multivariate Analysis.mp4 82MB
  25. 07 Increasing Performance with Vectorized Solutions/089 Calculating Model Accuracy.mp4 80MB
  26. 02 Algorithm Overview/031 Feature Selection with KNN.mp4 80MB
  27. 12 Image Recognition In Action/149 Implementing an Accuracy Gauge.mp4 80MB
  28. 09 Gradient Descent Alterations/110 Making Predictions with the Model.mp4 79MB
  29. 10 Natural Binary Classification/115 Decision Boundaries.mp4 79MB
  30. 02 Algorithm Overview/025 N-Dimension Distance.mp4 79MB
  31. 04 Applications of Tensorflow/047 KNN with Tensorflow.mp4 79MB
  32. 05 Getting Started with Gradient Descent/065 Derivatives.mp4 78MB
  33. 09 Gradient Descent Alterations/105 Batch and Stochastic Gradient Descent.mp4 77MB
  34. 07 Increasing Performance with Vectorized Solutions/099 Learning Rate Optimization.mp4 77MB
  35. 03 Onwards to Tensorflow JS/034 Lets Get Our Bearings.mp4 77MB
  36. 07 Increasing Performance with Vectorized Solutions/090 Implementing Coefficient of Determination.mp4 76MB
  37. 14 Appendix Custom CSV Loader/184 Splitting Test and Training.mp4 76MB
  38. 02 Algorithm Overview/028 Feature Normalization.mp4 73MB
  39. 07 Increasing Performance with Vectorized Solutions/085 Refactoring the Linear Regression Class.mp4 73MB
  40. 07 Increasing Performance with Vectorized Solutions/091 Dealing with Bad Accuracy.mp4 71MB
  41. 02 Algorithm Overview/026 Arbitrary Feature Spaces.mp4 71MB
  42. 02 Algorithm Overview/023 Updating KNN for Multiple Features.mp4 71MB
  43. 10 Natural Binary Classification/121 Updating Linear Regression for Logistic Regression.mp4 70MB
  44. 10 Natural Binary Classification/126 Variable Decision Boundaries.mp4 68MB
  45. 06 Gradient Descent with Tensorflow/080 Matrix Multiplication.mp4 67MB
  46. 09 Gradient Descent Alterations/108 Iterating Over Batches.mp4 67MB
  47. 06 Gradient Descent with Tensorflow/077 Calculating MSE Slopes.mp4 67MB
  48. 02 Algorithm Overview/029 Normalization with MinMax.mp4 67MB
  49. 09 Gradient Descent Alterations/109 Evaluating Batch Gradient Descent Results.mp4 66MB
  50. 07 Increasing Performance with Vectorized Solutions/087 A Few More Changes.mp4 66MB
  51. 11 Multi-Value Classification/138 Training a Multinominal Model.mp4 66MB
  52. 09 Gradient Descent Alterations/107 Determining Batch Size and Quantity.mp4 66MB
  53. 02 Algorithm Overview/032 Objective Feature Picking.mp4 66MB
  54. 05 Getting Started with Gradient Descent/067 Quick Breather and Review.mp4 66MB
  55. 02 Algorithm Overview/011 Lodash Review.mp4 65MB
  56. 04 Applications of Tensorflow/054 Reporting Error Percentages.mp4 64MB
  57. 02 Algorithm Overview/027 Magnitude Offsets in Features.mp4 64MB
  58. 06 Gradient Descent with Tensorflow/081 More on Matrix Multiplication.mp4 63MB
  59. 04 Applications of Tensorflow/049 Sorting Tensors.mp4 63MB
  60. 01 What is Machine Learning/002 Solving Machine Learning Problems.mp4 63MB
  61. 11 Multi-Value Classification/140 Sigmoid vs Softmax.mp4 63MB
  62. 06 Gradient Descent with Tensorflow/074 Default Algorithm Options.mp4 63MB
  63. 07 Increasing Performance with Vectorized Solutions/101 Updating Learning Rate.mp4 62MB
  64. 03 Onwards to Tensorflow JS/038 Broadcasting Operations.mp4 62MB
  65. 12 Image Recognition In Action/148 Encoding Label Values.mp4 62MB
  66. 08 Plotting Data with Javascript/103 Plotting MSE Values.mp4 61MB
  67. 10 Natural Binary Classification/112 Logistic Regression in Action.mp4 61MB
  68. 10 Natural Binary Classification/127 Mean Squared Error vs Cross Entropy.mp4 60MB
  69. 06 Gradient Descent with Tensorflow/082 Matrix Form of Slope Equations.mp4 60MB
  70. 10 Natural Binary Classification/117 Project Setup for Logistic Regression.mp4 59MB
  71. 02 Algorithm Overview/012 Implementing KNN.mp4 59MB
  72. 03 Onwards to Tensorflow JS/041 Creating Slices of Data.mp4 59MB
  73. 03 Onwards to Tensorflow JS/037 Elementwise Operations.mp4 58MB
  74. 04 Applications of Tensorflow/050 Averaging Top Values.mp4 58MB
  75. 07 Increasing Performance with Vectorized Solutions/094 Reapplying Standardization.mp4 58MB
  76. 12 Image Recognition In Action/147 Flattening Image Data.mp4 58MB
  77. 04 Applications of Tensorflow/048 Maintaining Order Relationships.mp4 58MB
  78. 14 Appendix Custom CSV Loader/182 Extracting Data Columns.mp4 57MB
  79. 06 Gradient Descent with Tensorflow/072 Project Overview.mp4 57MB
  80. 03 Onwards to Tensorflow JS/044 Massaging Dimensions with ExpandDims.mp4 57MB
  81. 13 Performance Optimization/158 Shallow vs Retained Memory Usage.mp4 57MB
  82. 05 Getting Started with Gradient Descent/064 Observations Around MSE.mp4 56MB
  83. 13 Performance Optimization/157 The Javascript Garbage Collector.mp4 56MB
  84. 10 Natural Binary Classification/113 Bad Equation Fits.mp4 55MB
  85. 12 Image Recognition In Action/145 Greyscale Values.mp4 55MB
  86. 09 Gradient Descent Alterations/106 Refactoring Towards Batch Gradient Descent.mp4 55MB
  87. 13 Performance Optimization/174 Improving Model Accuracy.mp4 55MB
  88. 04 Applications of Tensorflow/045 KNN with Regression.mp4 55MB
  89. 10 Natural Binary Classification/125 Implementing a Test Function.mp4 55MB
  90. 02 Algorithm Overview/019 Gauging Accuracy.mp4 54MB
  91. 04 Applications of Tensorflow/056 Numerical Standardization with Tensorflow.mp4 53MB
  92. 04 Applications of Tensorflow/053 Running an Analysis.mp4 52MB
  93. 02 Algorithm Overview/021 Refactoring Accuracy Reporting.mp4 52MB
  94. 14 Appendix Custom CSV Loader/183 Shuffling Data via Seed Phrase.mp4 52MB
  95. 07 Increasing Performance with Vectorized Solutions/100 Recording MSE History.mp4 52MB
  96. 05 Getting Started with Gradient Descent/061 Why Linear Regression.mp4 50MB
  97. 02 Algorithm Overview/013 Finishing KNN Implementation.mp4 50MB
  98. 11 Multi-Value Classification/132 A Smart Refactor to Multinominal Analysis.mp4 50MB
  99. 10 Natural Binary Classification/128 Refactoring with Cross Entropy.mp4 49MB
  100. 10 Natural Binary Classification/129 Finishing the Cost Refactor.mp4 49MB
  101. 13 Performance Optimization/156 Creating Memory Snapshots.mp4 49MB
  102. 11 Multi-Value Classification/141 Refactoring Sigmoid to Softmax.mp4 49MB
  103. 03 Onwards to Tensorflow JS/035 A Plan to Move Forward.mp4 49MB
  104. 10 Natural Binary Classification/120 Encoding Label Values.mp4 49MB
  105. 11 Multi-Value Classification/135 Refactoring to Multi-Column Weights.mp4 48MB
  106. 11 Multi-Value Classification/136 A Problem to Test Multinominal Classification.mp4 48MB
  107. 01 What is Machine Learning/007 Dataset Structures.mp4 48MB
  108. 12 Image Recognition In Action/152 Dealing with Zero Variances.mp4 48MB
  109. 07 Increasing Performance with Vectorized Solutions/095 Fixing Standardization Issues.mp4 48MB
  110. 08 Plotting Data with Javascript/104 Plotting MSE History against B Values.mp4 48MB
  111. 13 Performance Optimization/170 Plotting Cost History.mp4 48MB
  112. 01 What is Machine Learning/009 What Type of Problem.mp4 47MB
  113. 13 Performance Optimization/163 Tensorflows Eager Memory Usage.mp4 47MB
  114. 13 Performance Optimization/172 Fixing Cost History.mp4 47MB
  115. 13 Performance Optimization/171 NaN in Cost History.mp4 46MB
  116. 13 Performance Optimization/166 Tidying the Training Loop.mp4 46MB
  117. 08 Plotting Data with Javascript/102 Observing Changing Learning Rate and MSE.mp4 46MB
  118. 10 Natural Binary Classification/114 The Sigmoid Equation.mp4 45MB
  119. 02 Algorithm Overview/030 Applying Normalization.mp4 45MB
  120. 02 Algorithm Overview/016 Test and Training Data.mp4 45MB
  121. 02 Algorithm Overview/014 Testing the Algorithm.mp4 45MB
  122. 12 Image Recognition In Action/146 Many Features.mp4 45MB
  123. 11 Multi-Value Classification/137 Classifying Continuous Values.mp4 45MB
  124. 07 Increasing Performance with Vectorized Solutions/092 Reminder on Standardization.mp4 44MB
  125. 13 Performance Optimization/154 Handing Large Datasets.mp4 44MB
  126. 02 Algorithm Overview/024 Multi-Dimensional KNN.mp4 44MB
  127. 05 Getting Started with Gradient Descent/070 Gradient Descent with Multiple Terms.mp4 44MB
  128. 03 Onwards to Tensorflow JS/042 Tensor Concatenation.mp4 44MB
  129. 06 Gradient Descent with Tensorflow/073 Data Loading.mp4 43MB
  130. 13 Performance Optimization/161 Measuring Footprint Reduction.mp4 43MB
  131. 10 Natural Binary Classification/130 Plotting Changing Cost History.mp4 43MB
  132. 04 Applications of Tensorflow/059 What Now.mp4 42MB
  133. 04 Applications of Tensorflow/057 Applying Standardization.mp4 41MB
  134. 03 Onwards to Tensorflow JS/043 Summing Values Along an Axis.mp4 41MB
  135. 04 Applications of Tensorflow/046 A Change in Data Structure.mp4 41MB
  136. 05 Getting Started with Gradient Descent/069 Answering Common Questions.mp4 41MB
  137. 02 Algorithm Overview/015 Interpreting Bad Results.mp4 41MB
  138. 02 Algorithm Overview/018 Generalizing KNN.mp4 39MB
  139. 10 Natural Binary Classification/119 Importing Vehicle Data.mp4 39MB
  140. 11 Multi-Value Classification/133 A Smarter Refactor.mp4 38MB
  141. 13 Performance Optimization/155 Minimizing Memory Usage.mp4 38MB
  142. 13 Performance Optimization/165 Implementing TF Tidy.mp4 38MB
  143. 07 Increasing Performance with Vectorized Solutions/093 Data Processing in a Helper Method.mp4 37MB
  144. 14 Appendix Custom CSV Loader/181 Custom Value Parsing.mp4 37MB
  145. 10 Natural Binary Classification/124 Gauging Classification Accuracy.mp4 37MB
  146. 07 Increasing Performance with Vectorized Solutions/096 Massaging Learning Rates.mp4 36MB
  147. 13 Performance Optimization/169 Final Memory Report.mp4 36MB
  148. 02 Algorithm Overview/017 Randomizing Test Data.mp4 36MB
  149. 13 Performance Optimization/160 Releasing References.mp4 36MB
  150. 04 Applications of Tensorflow/051 Moving to the Editor.mp4 34MB
  151. 01 What is Machine Learning/006 Identifying Relevant Data.mp4 34MB
  152. 06 Gradient Descent with Tensorflow/078 Updating Coefficients.mp4 34MB
  153. 07 Increasing Performance with Vectorized Solutions/088 Same Results Or Not.mp4 34MB
  154. 02 Algorithm Overview/020 Printing a Report.mp4 33MB
  155. 10 Natural Binary Classification/122 The Sigmoid Equation with Logistic Regression.mp4 33MB
  156. 01 What is Machine Learning/008 Recording Observation Data.mp4 33MB
  157. 14 Appendix Custom CSV Loader/180 Parsing Number Values.mp4 31MB
  158. 11 Multi-Value Classification/143 Calculating Accuracy.mp4 31MB
  159. 01 What is Machine Learning/005 Problem Outline.mp4 31MB
  160. 03 Onwards to Tensorflow JS/040 Tensor Accessors.mp4 30MB
  161. 11 Multi-Value Classification/142 Implementing Accuracy Gauges.mp4 29MB
  162. 02 Algorithm Overview/033 Evaluating Different Feature Values.mp4 28MB
  163. 06 Gradient Descent with Tensorflow/075 Formulating the Training Loop.mp4 28MB
  164. 13 Performance Optimization/168 One More Optimization.mp4 27MB
  165. 03 Onwards to Tensorflow JS/039 Logging Tensor Data.mp4 26MB
  166. 12 Image Recognition In Action/153 Backfilling Variance.mp4 26MB
  167. 05 Getting Started with Gradient Descent/060 Linear Regression.mp4 25MB
  168. 11 Multi-Value Classification/131 Multinominal Logistic Regression.mp4 25MB
  169. 12 Image Recognition In Action/144 Handwriting Recognition.mp4 25MB
  170. 13 Performance Optimization/164 Cleaning up Tensors with Tidy.mp4 24MB
  171. 10 Natural Binary Classification/111 Introducing Logistic Regression.mp4 23MB
  172. 13 Performance Optimization/173 Massaging Learning Parameters.mp4 23MB
  173. 14 Appendix Custom CSV Loader/178 Splitting into Columns.mp4 20MB
  174. 12 Image Recognition In Action/150 Unchanging Accuracy.mp4 20MB
  175. 01 What is Machine Learning/004 App Setup.mp4 19MB
  176. 14 Appendix Custom CSV Loader/177 Reading Files from Disk.mp4 19MB
  177. 13 Performance Optimization/162 Optimization Tensorflow Memory Usage.mp4 19MB
  178. 14 Appendix Custom CSV Loader/179 Dropping Trailing Columns.mp4 18MB
  179. 13 Performance Optimization/167 Measuring Reduced Memory Usage.mp4 18MB
  180. 14 Appendix Custom CSV Loader/175 Loading CSV Files.mp4 16MB
  181. 10 Natural Binary Classification/116 Changes for Logistic Regression.mp4 12MB
  182. 14 Appendix Custom CSV Loader/176 A Test Dataset.mp4 10MB
  183. 01 What is Machine Learning/001 Getting Started - How to Get Help.mp4 8MB
  184. 10 Natural Binary Classification/118 regressions.zip 34KB
  185. 05 Getting Started with Gradient Descent/068 Why a Learning Rate.id.srt 28KB
  186. 05 Getting Started with Gradient Descent/068 Why a Learning Rate.en.srt 26KB
  187. 06 Gradient Descent with Tensorflow/084 How it All Works Together.id.srt 22KB
  188. 06 Gradient Descent with Tensorflow/084 How it All Works Together.en.srt 21KB
  189. 05 Getting Started with Gradient Descent/062 Understanding Gradient Descent.id.srt 21KB
  190. 03 Onwards to Tensorflow JS/036 Tensor Shape and Dimension.id.srt 20KB
  191. 05 Getting Started with Gradient Descent/062 Understanding Gradient Descent.en.srt 19KB
  192. 05 Getting Started with Gradient Descent/066 Gradient Descent in Action.id.srt 19KB
  193. 07 Increasing Performance with Vectorized Solutions/097 Moving Towards Multivariate Regression.id.srt 19KB
  194. 03 Onwards to Tensorflow JS/036 Tensor Shape and Dimension.en.srt 19KB
  195. 02 Algorithm Overview/022 Investigating Optimal K Values.id.srt 19KB
  196. 05 Getting Started with Gradient Descent/066 Gradient Descent in Action.en.srt 18KB
  197. 07 Increasing Performance with Vectorized Solutions/097 Moving Towards Multivariate Regression.en.srt 18KB
  198. 02 Algorithm Overview/022 Investigating Optimal K Values.en.srt 18KB
  199. 05 Getting Started with Gradient Descent/071 Multiple Terms in Action.id.srt 18KB
  200. 11 Multi-Value Classification/139 Marginal vs Conditional Probability.id.srt 17KB
  201. 05 Getting Started with Gradient Descent/071 Multiple Terms in Action.en.srt 17KB
  202. 06 Gradient Descent with Tensorflow/079 Interpreting Results.id.srt 16KB
  203. 02 Algorithm Overview/011 Lodash Review.id.srt 16KB
  204. 05 Getting Started with Gradient Descent/063 Guessing Coefficients with MSE.id.srt 16KB
  205. 11 Multi-Value Classification/139 Marginal vs Conditional Probability.en.srt 16KB
  206. 02 Algorithm Overview/025 N-Dimension Distance.id.srt 16KB
  207. 01 What is Machine Learning/003 A Complete Walkthrough.id.srt 16KB
  208. 11 Multi-Value Classification/134 A Single Instance Approach.id.srt 16KB
  209. 04 Applications of Tensorflow/052 Loading CSV Data.id.srt 16KB
  210. 04 Applications of Tensorflow/047 KNN with Tensorflow.id.srt 16KB
  211. 06 Gradient Descent with Tensorflow/079 Interpreting Results.en.srt 15KB
  212. 05 Getting Started with Gradient Descent/063 Guessing Coefficients with MSE.en.srt 15KB
  213. 11 Multi-Value Classification/134 A Single Instance Approach.en.srt 15KB
  214. 02 Algorithm Overview/025 N-Dimension Distance.en.srt 15KB
  215. 02 Algorithm Overview/011 Lodash Review.en.srt 15KB
  216. 06 Gradient Descent with Tensorflow/083 Simplification with Matrix Multiplication.id.srt 15KB
  217. 01 What is Machine Learning/003 A Complete Walkthrough.en.srt 15KB
  218. 04 Applications of Tensorflow/052 Loading CSV Data.en.srt 15KB
  219. 04 Applications of Tensorflow/047 KNN with Tensorflow.en.srt 15KB
  220. 06 Gradient Descent with Tensorflow/076 Initial Gradient Descent Implementation.id.srt 15KB
  221. 07 Increasing Performance with Vectorized Solutions/086 Refactoring to One Equation.id.srt 15KB
  222. 13 Performance Optimization/159 Measuring Memory Usage.id.srt 15KB
  223. 06 Gradient Descent with Tensorflow/083 Simplification with Matrix Multiplication.en.srt 14KB
  224. 06 Gradient Descent with Tensorflow/076 Initial Gradient Descent Implementation.en.srt 14KB
  225. 07 Increasing Performance with Vectorized Solutions/086 Refactoring to One Equation.en.srt 14KB
  226. 02 Algorithm Overview/026 Arbitrary Feature Spaces.id.srt 14KB
  227. 13 Performance Optimization/159 Measuring Memory Usage.en.srt 14KB
  228. 07 Increasing Performance with Vectorized Solutions/089 Calculating Model Accuracy.id.srt 14KB
  229. 02 Algorithm Overview/031 Feature Selection with KNN.id.srt 14KB
  230. 04 Applications of Tensorflow/058 Debugging Calculations.id.srt 14KB
  231. 02 Algorithm Overview/010 How K-Nearest Neighbor Works.id.srt 13KB
  232. 12 Image Recognition In Action/151 Debugging the Calculation Process.id.srt 13KB
  233. 02 Algorithm Overview/026 Arbitrary Feature Spaces.en.srt 13KB
  234. 07 Increasing Performance with Vectorized Solutions/099 Learning Rate Optimization.id.srt 13KB
  235. 07 Increasing Performance with Vectorized Solutions/089 Calculating Model Accuracy.en.srt 13KB
  236. 04 Applications of Tensorflow/058 Debugging Calculations.en.srt 13KB
  237. 02 Algorithm Overview/010 How K-Nearest Neighbor Works.en.srt 13KB
  238. 12 Image Recognition In Action/151 Debugging the Calculation Process.en.srt 13KB
  239. 06 Gradient Descent with Tensorflow/074 Default Algorithm Options.id.srt 13KB
  240. 04 Applications of Tensorflow/049 Sorting Tensors.id.srt 13KB
  241. 03 Onwards to Tensorflow JS/044 Massaging Dimensions with ExpandDims.id.srt 13KB
  242. 03 Onwards to Tensorflow JS/037 Elementwise Operations.id.srt 13KB
  243. 03 Onwards to Tensorflow JS/034 Lets Get Our Bearings.id.srt 13KB
  244. 02 Algorithm Overview/031 Feature Selection with KNN.en.srt 13KB
  245. 06 Gradient Descent with Tensorflow/074 Default Algorithm Options.en.srt 13KB
  246. 14 Appendix Custom CSV Loader/184 Splitting Test and Training.id.srt 13KB
  247. 09 Gradient Descent Alterations/108 Iterating Over Batches.id.srt 13KB
  248. 04 Applications of Tensorflow/050 Averaging Top Values.id.srt 13KB
  249. 07 Increasing Performance with Vectorized Solutions/099 Learning Rate Optimization.en.srt 13KB
  250. 10 Natural Binary Classification/115 Decision Boundaries.id.srt 13KB
  251. 07 Increasing Performance with Vectorized Solutions/098 Refactoring for Multivariate Analysis.id.srt 12KB
  252. 10 Natural Binary Classification/123 A Touch More Refactoring.id.srt 12KB
  253. 07 Increasing Performance with Vectorized Solutions/091 Dealing with Bad Accuracy.id.srt 12KB
  254. 09 Gradient Descent Alterations/110 Making Predictions with the Model.id.srt 12KB
  255. 03 Onwards to Tensorflow JS/044 Massaging Dimensions with ExpandDims.en.srt 12KB
  256. 03 Onwards to Tensorflow JS/034 Lets Get Our Bearings.en.srt 12KB
  257. 07 Increasing Performance with Vectorized Solutions/085 Refactoring the Linear Regression Class.id.srt 12KB
  258. 04 Applications of Tensorflow/056 Numerical Standardization with Tensorflow.id.srt 12KB
  259. 02 Algorithm Overview/028 Feature Normalization.id.srt 12KB
  260. 07 Increasing Performance with Vectorized Solutions/090 Implementing Coefficient of Determination.id.srt 12KB
  261. 10 Natural Binary Classification/126 Variable Decision Boundaries.id.srt 12KB
  262. 09 Gradient Descent Alterations/108 Iterating Over Batches.en.srt 12KB
  263. 04 Applications of Tensorflow/049 Sorting Tensors.en.srt 12KB
  264. 12 Image Recognition In Action/149 Implementing an Accuracy Gauge.id.srt 12KB
  265. 04 Applications of Tensorflow/055 Normalization or Standardization.id.srt 12KB
  266. 14 Appendix Custom CSV Loader/184 Splitting Test and Training.en.srt 12KB
  267. 09 Gradient Descent Alterations/105 Batch and Stochastic Gradient Descent.id.srt 12KB
  268. 07 Increasing Performance with Vectorized Solutions/098 Refactoring for Multivariate Analysis.en.srt 12KB
  269. 10 Natural Binary Classification/115 Decision Boundaries.en.srt 12KB
  270. 03 Onwards to Tensorflow JS/037 Elementwise Operations.en.srt 12KB
  271. 09 Gradient Descent Alterations/110 Making Predictions with the Model.en.srt 12KB
  272. 07 Increasing Performance with Vectorized Solutions/091 Dealing with Bad Accuracy.en.srt 12KB
  273. 04 Applications of Tensorflow/056 Numerical Standardization with Tensorflow.en.srt 12KB
  274. 10 Natural Binary Classification/123 A Touch More Refactoring.en.srt 12KB
  275. 03 Onwards to Tensorflow JS/041 Creating Slices of Data.id.srt 12KB
  276. 10 Natural Binary Classification/121 Updating Linear Regression for Logistic Regression.id.srt 12KB
  277. 04 Applications of Tensorflow/050 Averaging Top Values.en.srt 12KB
  278. 06 Gradient Descent with Tensorflow/080 Matrix Multiplication.id.srt 12KB
  279. 04 Applications of Tensorflow/055 Normalization or Standardization.en.srt 12KB
  280. 07 Increasing Performance with Vectorized Solutions/090 Implementing Coefficient of Determination.en.srt 12KB
  281. 02 Algorithm Overview/028 Feature Normalization.en.srt 12KB
  282. 07 Increasing Performance with Vectorized Solutions/085 Refactoring the Linear Regression Class.en.srt 12KB
  283. 03 Onwards to Tensorflow JS/041 Creating Slices of Data.en.srt 12KB
  284. 09 Gradient Descent Alterations/105 Batch and Stochastic Gradient Descent.en.srt 11KB
  285. 12 Image Recognition In Action/149 Implementing an Accuracy Gauge.en.srt 11KB
  286. 10 Natural Binary Classification/126 Variable Decision Boundaries.en.srt 11KB
  287. 06 Gradient Descent with Tensorflow/080 Matrix Multiplication.en.srt 11KB
  288. 05 Getting Started with Gradient Descent/065 Derivatives.id.srt 11KB
  289. 02 Algorithm Overview/012 Implementing KNN.id.srt 11KB
  290. 04 Applications of Tensorflow/048 Maintaining Order Relationships.id.srt 11KB
  291. 10 Natural Binary Classification/112 Logistic Regression in Action.id.srt 11KB
  292. 10 Natural Binary Classification/121 Updating Linear Regression for Logistic Regression.en.srt 11KB
  293. 03 Onwards to Tensorflow JS/038 Broadcasting Operations.id.srt 11KB
  294. 02 Algorithm Overview/029 Normalization with MinMax.id.srt 11KB
  295. 05 Getting Started with Gradient Descent/065 Derivatives.en.srt 11KB
  296. 10 Natural Binary Classification/112 Logistic Regression in Action.en.srt 11KB
  297. 07 Increasing Performance with Vectorized Solutions/101 Updating Learning Rate.id.srt 11KB
  298. 07 Increasing Performance with Vectorized Solutions/087 A Few More Changes.id.srt 11KB
  299. 03 Onwards to Tensorflow JS/038 Broadcasting Operations.en.srt 11KB
  300. 04 Applications of Tensorflow/048 Maintaining Order Relationships.en.srt 11KB
  301. 13 Performance Optimization/157 The Javascript Garbage Collector.id.srt 11KB
  302. 02 Algorithm Overview/023 Updating KNN for Multiple Features.id.srt 11KB
  303. 12 Image Recognition In Action/152 Dealing with Zero Variances.id.srt 11KB
  304. 02 Algorithm Overview/012 Implementing KNN.en.srt 11KB
  305. 02 Algorithm Overview/029 Normalization with MinMax.en.srt 10KB
  306. 02 Algorithm Overview/023 Updating KNN for Multiple Features.en.srt 10KB
  307. 11 Multi-Value Classification/138 Training a Multinominal Model.id.srt 10KB
  308. 11 Multi-Value Classification/140 Sigmoid vs Softmax.id.srt 10KB
  309. 06 Gradient Descent with Tensorflow/077 Calculating MSE Slopes.id.srt 10KB
  310. 13 Performance Optimization/157 The Javascript Garbage Collector.en.srt 10KB
  311. 06 Gradient Descent with Tensorflow/082 Matrix Form of Slope Equations.id.srt 10KB
  312. 07 Increasing Performance with Vectorized Solutions/087 A Few More Changes.en.srt 10KB
  313. 07 Increasing Performance with Vectorized Solutions/101 Updating Learning Rate.en.srt 10KB
  314. 12 Image Recognition In Action/152 Dealing with Zero Variances.en.srt 10KB
  315. 02 Algorithm Overview/032 Objective Feature Picking.id.srt 10KB
  316. 04 Applications of Tensorflow/054 Reporting Error Percentages.id.srt 10KB
  317. 05 Getting Started with Gradient Descent/067 Quick Breather and Review.id.srt 10KB
  318. 11 Multi-Value Classification/138 Training a Multinominal Model.en.srt 10KB
  319. 11 Multi-Value Classification/140 Sigmoid vs Softmax.en.srt 10KB
  320. 06 Gradient Descent with Tensorflow/081 More on Matrix Multiplication.id.srt 10KB
  321. 09 Gradient Descent Alterations/109 Evaluating Batch Gradient Descent Results.id.srt 10KB
  322. 01 What is Machine Learning/002 Solving Machine Learning Problems.id.srt 10KB
  323. 04 Applications of Tensorflow/053 Running an Analysis.id.srt 10KB
  324. 05 Getting Started with Gradient Descent/064 Observations Around MSE.id.srt 10KB
  325. 06 Gradient Descent with Tensorflow/072 Project Overview.id.srt 10KB
  326. 06 Gradient Descent with Tensorflow/077 Calculating MSE Slopes.en.srt 10KB
  327. 06 Gradient Descent with Tensorflow/082 Matrix Form of Slope Equations.en.srt 10KB
  328. 10 Natural Binary Classification/117 Project Setup for Logistic Regression.id.srt 10KB
  329. 13 Performance Optimization/158 Shallow vs Retained Memory Usage.id.srt 10KB
  330. 01 What is Machine Learning/007 Dataset Structures.id.srt 9KB
  331. 06 Gradient Descent with Tensorflow/072 Project Overview.en.srt 9KB
  332. 06 Gradient Descent with Tensorflow/081 More on Matrix Multiplication.en.srt 9KB
  333. 02 Algorithm Overview/032 Objective Feature Picking.en.srt 9KB
  334. 07 Increasing Performance with Vectorized Solutions/095 Fixing Standardization Issues.id.srt 9KB
  335. 02 Algorithm Overview/013 Finishing KNN Implementation.id.srt 9KB
  336. 04 Applications of Tensorflow/053 Running an Analysis.en.srt 9KB
  337. 04 Applications of Tensorflow/054 Reporting Error Percentages.en.srt 9KB
  338. 05 Getting Started with Gradient Descent/064 Observations Around MSE.en.srt 9KB
  339. 10 Natural Binary Classification/127 Mean Squared Error vs Cross Entropy.id.srt 9KB
  340. 01 What is Machine Learning/002 Solving Machine Learning Problems.en.srt 9KB
  341. 10 Natural Binary Classification/117 Project Setup for Logistic Regression.en.srt 9KB
  342. 12 Image Recognition In Action/147 Flattening Image Data.id.srt 9KB
  343. 01 What is Machine Learning/007 Dataset Structures.en.srt 9KB
  344. 05 Getting Started with Gradient Descent/067 Quick Breather and Review.en.srt 9KB
  345. 10 Natural Binary Classification/113 Bad Equation Fits.id.srt 9KB
  346. 09 Gradient Descent Alterations/107 Determining Batch Size and Quantity.id.srt 9KB
  347. 13 Performance Optimization/158 Shallow vs Retained Memory Usage.en.srt 9KB
  348. 09 Gradient Descent Alterations/109 Evaluating Batch Gradient Descent Results.en.srt 9KB
  349. 07 Increasing Performance with Vectorized Solutions/094 Reapplying Standardization.id.srt 9KB
  350. 10 Natural Binary Classification/125 Implementing a Test Function.id.srt 9KB
  351. 03 Onwards to Tensorflow JS/040 Tensor Accessors.id.srt 9KB
  352. 02 Algorithm Overview/027 Magnitude Offsets in Features.id.srt 9KB
  353. 14 Appendix Custom CSV Loader/183 Shuffling Data via Seed Phrase.id.srt 9KB
  354. 12 Image Recognition In Action/148 Encoding Label Values.id.srt 9KB
  355. 07 Increasing Performance with Vectorized Solutions/095 Fixing Standardization Issues.en.srt 9KB
  356. 10 Natural Binary Classification/127 Mean Squared Error vs Cross Entropy.en.srt 9KB
  357. 12 Image Recognition In Action/147 Flattening Image Data.en.srt 9KB
  358. 08 Plotting Data with Javascript/103 Plotting MSE Values.id.srt 9KB
  359. 02 Algorithm Overview/013 Finishing KNN Implementation.en.srt 9KB
  360. 09 Gradient Descent Alterations/107 Determining Batch Size and Quantity.en.srt 9KB
  361. 03 Onwards to Tensorflow JS/042 Tensor Concatenation.id.srt 9KB
  362. 11 Multi-Value Classification/132 A Smart Refactor to Multinominal Analysis.id.srt 9KB
  363. 02 Algorithm Overview/027 Magnitude Offsets in Features.en.srt 9KB
  364. 03 Onwards to Tensorflow JS/043 Summing Values Along an Axis.id.srt 9KB
  365. 10 Natural Binary Classification/113 Bad Equation Fits.en.srt 9KB
  366. 13 Performance Optimization/156 Creating Memory Snapshots.id.srt 9KB
  367. 07 Increasing Performance with Vectorized Solutions/100 Recording MSE History.id.srt 9KB
  368. 10 Natural Binary Classification/125 Implementing a Test Function.en.srt 9KB
  369. 03 Onwards to Tensorflow JS/040 Tensor Accessors.en.srt 9KB
  370. 02 Algorithm Overview/019 Gauging Accuracy.id.srt 9KB
  371. 03 Onwards to Tensorflow JS/042 Tensor Concatenation.en.srt 9KB
  372. 07 Increasing Performance with Vectorized Solutions/094 Reapplying Standardization.en.srt 9KB
  373. 10 Natural Binary Classification/128 Refactoring with Cross Entropy.id.srt 8KB
  374. 12 Image Recognition In Action/148 Encoding Label Values.en.srt 8KB
  375. 14 Appendix Custom CSV Loader/183 Shuffling Data via Seed Phrase.en.srt 8KB
  376. 09 Gradient Descent Alterations/106 Refactoring Towards Batch Gradient Descent.id.srt 8KB
  377. 12 Image Recognition In Action/145 Greyscale Values.id.srt 8KB
  378. 03 Onwards to Tensorflow JS/043 Summing Values Along an Axis.en.srt 8KB
  379. 03 Onwards to Tensorflow JS/035 A Plan to Move Forward.id.srt 8KB
  380. 11 Multi-Value Classification/132 A Smart Refactor to Multinominal Analysis.en.srt 8KB
  381. 08 Plotting Data with Javascript/103 Plotting MSE Values.en.srt 8KB
  382. 04 Applications of Tensorflow/045 KNN with Regression.id.srt 8KB
  383. 10 Natural Binary Classification/128 Refactoring with Cross Entropy.en.srt 8KB
  384. 06 Gradient Descent with Tensorflow/073 Data Loading.id.srt 8KB
  385. 13 Performance Optimization/156 Creating Memory Snapshots.en.srt 8KB
  386. 07 Increasing Performance with Vectorized Solutions/100 Recording MSE History.en.srt 8KB
  387. 02 Algorithm Overview/021 Refactoring Accuracy Reporting.id.srt 8KB
  388. 14 Appendix Custom CSV Loader/182 Extracting Data Columns.id.srt 8KB
  389. 09 Gradient Descent Alterations/106 Refactoring Towards Batch Gradient Descent.en.srt 8KB
  390. 05 Getting Started with Gradient Descent/061 Why Linear Regression.id.srt 8KB
  391. 04 Applications of Tensorflow/045 KNN with Regression.en.srt 8KB
  392. 02 Algorithm Overview/019 Gauging Accuracy.en.srt 8KB
  393. 12 Image Recognition In Action/145 Greyscale Values.en.srt 8KB
  394. 11 Multi-Value Classification/141 Refactoring Sigmoid to Softmax.id.srt 8KB
  395. 11 Multi-Value Classification/135 Refactoring to Multi-Column Weights.id.srt 8KB
  396. 05 Getting Started with Gradient Descent/070 Gradient Descent with Multiple Terms.id.srt 8KB
  397. 01 What is Machine Learning/009 What Type of Problem.id.srt 8KB
  398. 13 Performance Optimization/155 Minimizing Memory Usage.id.srt 8KB
  399. 06 Gradient Descent with Tensorflow/073 Data Loading.en.srt 8KB
  400. 14 Appendix Custom CSV Loader/182 Extracting Data Columns.en.srt 8KB
  401. 03 Onwards to Tensorflow JS/035 A Plan to Move Forward.en.srt 8KB
  402. 10 Natural Binary Classification/114 The Sigmoid Equation.id.srt 8KB
  403. 11 Multi-Value Classification/135 Refactoring to Multi-Column Weights.en.srt 8KB
  404. 13 Performance Optimization/172 Fixing Cost History.id.srt 8KB
  405. 05 Getting Started with Gradient Descent/061 Why Linear Regression.en.srt 8KB
  406. 02 Algorithm Overview/021 Refactoring Accuracy Reporting.en.srt 8KB
  407. 01 What is Machine Learning/009 What Type of Problem.en.srt 8KB
  408. 13 Performance Optimization/154 Handing Large Datasets.id.srt 8KB
  409. 11 Multi-Value Classification/136 A Problem to Test Multinominal Classification.id.srt 8KB
  410. 11 Multi-Value Classification/141 Refactoring Sigmoid to Softmax.en.srt 8KB
  411. 13 Performance Optimization/155 Minimizing Memory Usage.en.srt 7KB
  412. 02 Algorithm Overview/014 Testing the Algorithm.id.srt 7KB
  413. 05 Getting Started with Gradient Descent/070 Gradient Descent with Multiple Terms.en.srt 7KB
  414. 11 Multi-Value Classification/137 Classifying Continuous Values.id.srt 7KB
  415. 02 Algorithm Overview/030 Applying Normalization.id.srt 7KB
  416. 13 Performance Optimization/163 Tensorflows Eager Memory Usage.id.srt 7KB
  417. 08 Plotting Data with Javascript/104 Plotting MSE History against B Values.id.srt 7KB
  418. 07 Increasing Performance with Vectorized Solutions/092 Reminder on Standardization.id.srt 7KB
  419. 13 Performance Optimization/171 NaN in Cost History.id.srt 7KB
  420. 10 Natural Binary Classification/114 The Sigmoid Equation.en.srt 7KB
  421. 10 Natural Binary Classification/129 Finishing the Cost Refactor.id.srt 7KB
  422. 08 Plotting Data with Javascript/102 Observing Changing Learning Rate and MSE.id.srt 7KB
  423. 13 Performance Optimization/174 Improving Model Accuracy.id.srt 7KB
  424. 11 Multi-Value Classification/136 A Problem to Test Multinominal Classification.en.srt 7KB
  425. 13 Performance Optimization/172 Fixing Cost History.en.srt 7KB
  426. 10 Natural Binary Classification/122 The Sigmoid Equation with Logistic Regression.id.srt 7KB
  427. 02 Algorithm Overview/014 Testing the Algorithm.en.srt 7KB
  428. 10 Natural Binary Classification/120 Encoding Label Values.id.srt 7KB
  429. 08 Plotting Data with Javascript/104 Plotting MSE History against B Values.en.srt 7KB
  430. 13 Performance Optimization/154 Handing Large Datasets.en.srt 7KB
  431. 11 Multi-Value Classification/137 Classifying Continuous Values.en.srt 7KB
  432. 07 Increasing Performance with Vectorized Solutions/092 Reminder on Standardization.en.srt 7KB
  433. 13 Performance Optimization/163 Tensorflows Eager Memory Usage.en.srt 7KB
  434. 13 Performance Optimization/170 Plotting Cost History.id.srt 7KB
  435. 02 Algorithm Overview/030 Applying Normalization.en.srt 7KB
  436. 13 Performance Optimization/171 NaN in Cost History.en.srt 7KB
  437. 10 Natural Binary Classification/119 Importing Vehicle Data.id.srt 7KB
  438. 10 Natural Binary Classification/120 Encoding Label Values.en.srt 7KB
  439. 10 Natural Binary Classification/129 Finishing the Cost Refactor.en.srt 7KB
  440. 14 Appendix Custom CSV Loader/181 Custom Value Parsing.id.srt 7KB
  441. 04 Applications of Tensorflow/059 What Now.id.srt 7KB
  442. 01 What is Machine Learning/006 Identifying Relevant Data.id.srt 7KB
  443. 08 Plotting Data with Javascript/102 Observing Changing Learning Rate and MSE.en.srt 7KB
  444. 04 Applications of Tensorflow/046 A Change in Data Structure.id.srt 7KB
  445. 10 Natural Binary Classification/122 The Sigmoid Equation with Logistic Regression.en.srt 7KB
  446. 13 Performance Optimization/174 Improving Model Accuracy.en.srt 7KB
  447. 01 What is Machine Learning/006 Identifying Relevant Data.en.srt 7KB
  448. 10 Natural Binary Classification/119 Importing Vehicle Data.en.srt 7KB
  449. 02 Algorithm Overview/015 Interpreting Bad Results.id.srt 7KB
  450. 13 Performance Optimization/161 Measuring Footprint Reduction.id.srt 7KB
  451. 03 Onwards to Tensorflow JS/039 Logging Tensor Data.id.srt 7KB
  452. 13 Performance Optimization/170 Plotting Cost History.en.srt 7KB
  453. 04 Applications of Tensorflow/046 A Change in Data Structure.en.srt 7KB
  454. 14 Appendix Custom CSV Loader/181 Custom Value Parsing.en.srt 7KB
  455. 02 Algorithm Overview/015 Interpreting Bad Results.en.srt 6KB
  456. 02 Algorithm Overview/024 Multi-Dimensional KNN.id.srt 6KB
  457. 02 Algorithm Overview/016 Test and Training Data.id.srt 6KB
  458. 13 Performance Optimization/166 Tidying the Training Loop.id.srt 6KB
  459. 04 Applications of Tensorflow/059 What Now.en.srt 6KB
  460. 04 Applications of Tensorflow/057 Applying Standardization.id.srt 6KB
  461. 05 Getting Started with Gradient Descent/069 Answering Common Questions.id.srt 6KB
  462. 02 Algorithm Overview/024 Multi-Dimensional KNN.en.srt 6KB
  463. 01 What is Machine Learning/008 Recording Observation Data.id.srt 6KB
  464. 13 Performance Optimization/166 Tidying the Training Loop.en.srt 6KB
  465. 03 Onwards to Tensorflow JS/039 Logging Tensor Data.en.srt 6KB
  466. 13 Performance Optimization/161 Measuring Footprint Reduction.en.srt 6KB
  467. 04 Applications of Tensorflow/057 Applying Standardization.en.srt 6KB
  468. 11 Multi-Value Classification/133 A Smarter Refactor.id.srt 6KB
  469. 02 Algorithm Overview/016 Test and Training Data.en.srt 6KB
  470. 01 What is Machine Learning/008 Recording Observation Data.en.srt 6KB
  471. 10 Natural Binary Classification/130 Plotting Changing Cost History.id.srt 6KB
  472. 02 Algorithm Overview/017 Randomizing Test Data.id.srt 6KB
  473. 02 Algorithm Overview/018 Generalizing KNN.id.srt 6KB
  474. 05 Getting Started with Gradient Descent/069 Answering Common Questions.en.srt 6KB
  475. 11 Multi-Value Classification/133 A Smarter Refactor.en.srt 6KB
  476. 07 Increasing Performance with Vectorized Solutions/088 Same Results Or Not.id.srt 6KB
  477. 07 Increasing Performance with Vectorized Solutions/093 Data Processing in a Helper Method.id.srt 6KB
  478. 02 Algorithm Overview/017 Randomizing Test Data.en.srt 6KB
  479. 10 Natural Binary Classification/130 Plotting Changing Cost History.en.srt 6KB
  480. 14 Appendix Custom CSV Loader/180 Parsing Number Values.id.srt 6KB
  481. 02 Algorithm Overview/018 Generalizing KNN.en.srt 6KB
  482. 13 Performance Optimization/165 Implementing TF Tidy.id.srt 6KB
  483. 10 Natural Binary Classification/124 Gauging Classification Accuracy.id.srt 6KB
  484. 07 Increasing Performance with Vectorized Solutions/093 Data Processing in a Helper Method.en.srt 6KB
  485. 12 Image Recognition In Action/146 Many Features.id.srt 5KB
  486. 14 Appendix Custom CSV Loader/180 Parsing Number Values.en.srt 5KB
  487. 07 Increasing Performance with Vectorized Solutions/088 Same Results Or Not.en.srt 5KB
  488. 10 Natural Binary Classification/124 Gauging Classification Accuracy.en.srt 5KB
  489. 13 Performance Optimization/165 Implementing TF Tidy.en.srt 5KB
  490. 02 Algorithm Overview/020 Printing a Report.id.srt 5KB
  491. 11 Multi-Value Classification/143 Calculating Accuracy.id.srt 5KB
  492. 12 Image Recognition In Action/146 Many Features.en.srt 5KB
  493. 04 Applications of Tensorflow/051 Moving to the Editor.id.srt 5KB
  494. 06 Gradient Descent with Tensorflow/078 Updating Coefficients.id.srt 5KB
  495. 04 Applications of Tensorflow/051 Moving to the Editor.en.srt 5KB
  496. 06 Gradient Descent with Tensorflow/075 Formulating the Training Loop.id.srt 5KB
  497. 13 Performance Optimization/160 Releasing References.id.srt 5KB
  498. 11 Multi-Value Classification/143 Calculating Accuracy.en.srt 5KB
  499. 01 What is Machine Learning/005 Problem Outline.id.srt 5KB
  500. 06 Gradient Descent with Tensorflow/075 Formulating the Training Loop.en.srt 5KB
  501. 07 Increasing Performance with Vectorized Solutions/096 Massaging Learning Rates.id.srt 5KB
  502. 02 Algorithm Overview/020 Printing a Report.en.srt 5KB
  503. 06 Gradient Descent with Tensorflow/078 Updating Coefficients.en.srt 5KB
  504. 13 Performance Optimization/160 Releasing References.en.srt 5KB
  505. 01 What is Machine Learning/005 Problem Outline.en.srt 5KB
  506. 05 Getting Started with Gradient Descent/060 Linear Regression.id.srt 5KB
  507. 13 Performance Optimization/169 Final Memory Report.id.srt 5KB
  508. 07 Increasing Performance with Vectorized Solutions/096 Massaging Learning Rates.en.srt 5KB
  509. 13 Performance Optimization/164 Cleaning up Tensors with Tidy.id.srt 5KB
  510. 14 Appendix Custom CSV Loader/177 Reading Files from Disk.id.srt 4KB
  511. 05 Getting Started with Gradient Descent/060 Linear Regression.en.srt 4KB
  512. 13 Performance Optimization/169 Final Memory Report.en.srt 4KB
  513. 14 Appendix Custom CSV Loader/177 Reading Files from Disk.en.srt 4KB
  514. 14 Appendix Custom CSV Loader/178 Splitting into Columns.id.srt 4KB
  515. 13 Performance Optimization/164 Cleaning up Tensors with Tidy.en.srt 4KB
  516. 02 Algorithm Overview/033 Evaluating Different Feature Values.id.srt 4KB
  517. 11 Multi-Value Classification/142 Implementing Accuracy Gauges.id.srt 4KB
  518. 12 Image Recognition In Action/153 Backfilling Variance.id.srt 4KB
  519. 11 Multi-Value Classification/142 Implementing Accuracy Gauges.en.srt 4KB
  520. 14 Appendix Custom CSV Loader/178 Splitting into Columns.en.srt 4KB
  521. 02 Algorithm Overview/033 Evaluating Different Feature Values.en.srt 4KB
  522. 14 Appendix Custom CSV Loader/179 Dropping Trailing Columns.id.srt 4KB
  523. 12 Image Recognition In Action/153 Backfilling Variance.en.srt 4KB
  524. 10 Natural Binary Classification/111 Introducing Logistic Regression.id.srt 4KB
  525. 10 Natural Binary Classification/111 Introducing Logistic Regression.en.srt 4KB
  526. 13 Performance Optimization/168 One More Optimization.id.srt 4KB
  527. 14 Appendix Custom CSV Loader/179 Dropping Trailing Columns.en.srt 4KB
  528. 11 Multi-Value Classification/131 Multinominal Logistic Regression.id.srt 4KB
  529. 13 Performance Optimization/168 One More Optimization.en.srt 4KB
  530. 12 Image Recognition In Action/144 Handwriting Recognition.id.srt 4KB
  531. 01 What is Machine Learning/004 App Setup.id.srt 4KB
  532. 15 Extras/185 Bonus.html 4KB
  533. 11 Multi-Value Classification/131 Multinominal Logistic Regression.en.srt 4KB
  534. 12 Image Recognition In Action/144 Handwriting Recognition.en.srt 4KB
  535. 14 Appendix Custom CSV Loader/175 Loading CSV Files.id.srt 4KB
  536. 01 What is Machine Learning/004 App Setup.en.srt 3KB
  537. 12 Image Recognition In Action/150 Unchanging Accuracy.id.srt 3KB
  538. 14 Appendix Custom CSV Loader/175 Loading CSV Files.en.srt 3KB
  539. 12 Image Recognition In Action/150 Unchanging Accuracy.en.srt 3KB
  540. 13 Performance Optimization/173 Massaging Learning Parameters.id.srt 3KB
  541. 14 Appendix Custom CSV Loader/176 A Test Dataset.id.srt 3KB
  542. 14 Appendix Custom CSV Loader/176 A Test Dataset.en.srt 3KB
  543. 13 Performance Optimization/162 Optimization Tensorflow Memory Usage.id.srt 3KB
  544. 13 Performance Optimization/173 Massaging Learning Parameters.en.srt 3KB
  545. 13 Performance Optimization/162 Optimization Tensorflow Memory Usage.en.srt 3KB
  546. 13 Performance Optimization/167 Measuring Reduced Memory Usage.id.srt 3KB
  547. 13 Performance Optimization/167 Measuring Reduced Memory Usage.en.srt 2KB
  548. 10 Natural Binary Classification/116 Changes for Logistic Regression.id.srt 2KB
  549. 10 Natural Binary Classification/116 Changes for Logistic Regression.en.srt 2KB
  550. 01 What is Machine Learning/001 Getting Started - How to Get Help.id.srt 2KB
  551. 01 What is Machine Learning/001 Getting Started - How to Get Help.en.srt 2KB
  552. 10 Natural Binary Classification/118 Project Download.html 1KB
  553. [FreeCourseWorld.Com].url 54B
  554. [DesireCourse.Net].url 51B
  555. [CourseClub.Me].url 48B