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[] Udemy - Machine Learning with Javascript

  • 收录时间:2019-11-16 04:51:15
  • 文件大小:10GB
  • 下载次数:25
  • 最近下载:2021-01-20 15:08:27
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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 fro 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-en.srt 26KB
  186. 06 Gradient Descent with Tensorflow/084 How it All Works Together-en.srt 21KB
  187. 05 Getting Started with Gradient Descent/062 Understanding Gradient Descent-en.srt 19KB
  188. 03 Onwards to Tensorflow JS/036 Tensor Shape and Dimension-en.srt 19KB
  189. 05 Getting Started with Gradient Descent/066 Gradient Descent in Action-en.srt 18KB
  190. 07 Increasing Performance with Vectorized Solutions/097 Moving Towards Multivariate Regression-en.srt 18KB
  191. 02 Algorithm Overview/022 Investigating Optimal K Values-en.srt 18KB
  192. 05 Getting Started with Gradient Descent/071 Multiple Terms in Action-en.srt 17KB
  193. 11 Multi-Value Classification/139 Marginal vs Conditional Probability-en.srt 16KB
  194. 06 Gradient Descent with Tensorflow/079 Interpreting Results-en.srt 15KB
  195. 05 Getting Started with Gradient Descent/063 Guessing Coefficients with MSE-en.srt 15KB
  196. 11 Multi-Value Classification/134 A Single Instance Approach-en.srt 15KB
  197. 02 Algorithm Overview/025 N-Dimension Distance-en.srt 15KB
  198. 02 Algorithm Overview/011 Lodash Review-en.srt 15KB
  199. 01 What is Machine Learning/003 A Complete Walkthrough-en.srt 15KB
  200. 04 Applications of Tensorflow/052 Loading CSV Data-en.srt 15KB
  201. 04 Applications of Tensorflow/047 KNN with Tensorflow-en.srt 15KB
  202. 06 Gradient Descent with Tensorflow/083 Simplification with Matrix Multiplication-en.srt 14KB
  203. 06 Gradient Descent with Tensorflow/076 Initial Gradient Descent Implementation-en.srt 14KB
  204. 07 Increasing Performance with Vectorized Solutions/086 Refactoring to One Equation-en.srt 14KB
  205. 13 Performance Optimization/159 Measuring Memory Usage-en.srt 14KB
  206. 02 Algorithm Overview/026 Arbitrary Feature Spaces-en.srt 13KB
  207. 07 Increasing Performance with Vectorized Solutions/089 Calculating Model Accuracy-en.srt 13KB
  208. 04 Applications of Tensorflow/058 Debugging Calculations-en.srt 13KB
  209. 02 Algorithm Overview/010 How K-Nearest Neighbor Works-en.srt 13KB
  210. 12 Image Recognition In Action/151 Debugging the Calculation Process-en.srt 13KB
  211. 02 Algorithm Overview/031 Feature Selection with KNN-en.srt 13KB
  212. 06 Gradient Descent with Tensorflow/074 Default Algorithm Options-en.srt 13KB
  213. 07 Increasing Performance with Vectorized Solutions/099 Learning Rate Optimization-en.srt 13KB
  214. 03 Onwards to Tensorflow JS/044 Massaging Dimensions with ExpandDims-en.srt 12KB
  215. 03 Onwards to Tensorflow JS/034 Lets Get Our Bearings-en.srt 12KB
  216. 09 Gradient Descent Alterations/108 Iterating Over Batches-en.srt 12KB
  217. 04 Applications of Tensorflow/049 Sorting Tensors-en.srt 12KB
  218. 14 Appendix Custom CSV Loader/184 Splitting Test and Training-en.srt 12KB
  219. 07 Increasing Performance with Vectorized Solutions/098 Refactoring for Multivariate Analysis-en.srt 12KB
  220. 10 Natural Binary Classification/115 Decision Boundaries-en.srt 12KB
  221. 03 Onwards to Tensorflow JS/037 Elementwise Operations-en.srt 12KB
  222. 09 Gradient Descent Alterations/110 Making Predictions with the Model-en.srt 12KB
  223. 07 Increasing Performance with Vectorized Solutions/091 Dealing with Bad Accuracy-en.srt 12KB
  224. 04 Applications of Tensorflow/056 Numerical Standardization with Tensorflow-en.srt 12KB
  225. 10 Natural Binary Classification/123 A Touch More Refactoring-en.srt 12KB
  226. 04 Applications of Tensorflow/050 Averaging Top Values-en.srt 12KB
  227. 04 Applications of Tensorflow/055 Normalization or Standardization-en.srt 12KB
  228. 07 Increasing Performance with Vectorized Solutions/090 Implementing Coefficient of Determination-en.srt 12KB
  229. 02 Algorithm Overview/028 Feature Normalization-en.srt 12KB
  230. 07 Increasing Performance with Vectorized Solutions/085 Refactoring the Linear Regression Class-en.srt 12KB
  231. 03 Onwards to Tensorflow JS/041 Creating Slices of Data-en.srt 12KB
  232. 09 Gradient Descent Alterations/105 Batch and Stochastic Gradient Descent-en.srt 11KB
  233. 12 Image Recognition In Action/149 Implementing an Accuracy Gauge-en.srt 11KB
  234. 10 Natural Binary Classification/126 Variable Decision Boundaries-en.srt 11KB
  235. 06 Gradient Descent with Tensorflow/080 Matrix Multiplication-en.srt 11KB
  236. 10 Natural Binary Classification/121 Updating Linear Regression fro Logistic Regression-en.srt 11KB
  237. 05 Getting Started with Gradient Descent/065 Derivatives-en.srt 11KB
  238. 10 Natural Binary Classification/112 Logistic Regression in Action-en.srt 11KB
  239. 03 Onwards to Tensorflow JS/038 Broadcasting Operations-en.srt 11KB
  240. 04 Applications of Tensorflow/048 Maintaining Order Relationships-en.srt 11KB
  241. 02 Algorithm Overview/012 Implementing KNN-en.srt 11KB
  242. 02 Algorithm Overview/029 Normalization with MinMax-en.srt 10KB
  243. 02 Algorithm Overview/023 Updating KNN for Multiple Features-en.srt 10KB
  244. 13 Performance Optimization/157 The Javascript Garbage Collector-en.srt 10KB
  245. 07 Increasing Performance with Vectorized Solutions/087 A Few More Changes-en.srt 10KB
  246. 07 Increasing Performance with Vectorized Solutions/101 Updating Learning Rate-en.srt 10KB
  247. 12 Image Recognition In Action/152 Dealing with Zero Variances-en.srt 10KB
  248. 11 Multi-Value Classification/138 Training a Multinominal Model-en.srt 10KB
  249. 11 Multi-Value Classification/140 Sigmoid vs Softmax-en.srt 10KB
  250. 06 Gradient Descent with Tensorflow/077 Calculating MSE Slopes-en.srt 10KB
  251. 06 Gradient Descent with Tensorflow/082 Matrix Form of Slope Equations-en.srt 10KB
  252. 06 Gradient Descent with Tensorflow/072 Project Overview-en.srt 9KB
  253. 06 Gradient Descent with Tensorflow/081 More on Matrix Multiplication-en.srt 9KB
  254. 02 Algorithm Overview/032 Objective Feature Picking-en.srt 9KB
  255. 04 Applications of Tensorflow/053 Running an Analysis-en.srt 9KB
  256. 04 Applications of Tensorflow/054 Reporting Error Percentages-en.srt 9KB
  257. 05 Getting Started with Gradient Descent/064 Observations Around MSE-en.srt 9KB
  258. 01 What is Machine Learning/002 Solving Machine Learning Problems-en.srt 9KB
  259. 10 Natural Binary Classification/117 Project Setup for Logistic Regression-en.srt 9KB
  260. 01 What is Machine Learning/007 Dataset Structures-en.srt 9KB
  261. 05 Getting Started with Gradient Descent/067 Quick Breather and Review-en.srt 9KB
  262. 13 Performance Optimization/158 Shallow vs Retained Memory Usage-en.srt 9KB
  263. 09 Gradient Descent Alterations/109 Evaluating Batch Gradient Descent Results-en.srt 9KB
  264. 07 Increasing Performance with Vectorized Solutions/095 Fixing Standardization Issues-en.srt 9KB
  265. 10 Natural Binary Classification/127 Mean Squared Error vs Cross Entropy-en.srt 9KB
  266. 12 Image Recognition In Action/147 Flattening Image Data-en.srt 9KB
  267. 02 Algorithm Overview/013 Finishing KNN Implementation-en.srt 9KB
  268. 09 Gradient Descent Alterations/107 Determining Batch Size and Quantity-en.srt 9KB
  269. 02 Algorithm Overview/027 Magnitude Offsets in Features-en.srt 9KB
  270. 10 Natural Binary Classification/113 Bad Equation Fits-en.srt 9KB
  271. 10 Natural Binary Classification/125 Implementing a Test Function-en.srt 9KB
  272. 03 Onwards to Tensorflow JS/040 Tensor Accessors-en.srt 9KB
  273. 03 Onwards to Tensorflow JS/042 Tensor Concatenation-en.srt 9KB
  274. 07 Increasing Performance with Vectorized Solutions/094 Reapplying Standardization-en.srt 9KB
  275. 12 Image Recognition In Action/148 Encoding Label Values-en.srt 8KB
  276. 14 Appendix Custom CSV Loader/183 Shuffling Data via Seed Phrase-en.srt 8KB
  277. 03 Onwards to Tensorflow JS/043 Summing Values Along an Axis-en.srt 8KB
  278. 11 Multi-Value Classification/132 A Smart Refactor to Multinominal Analysis-en.srt 8KB
  279. 08 Plotting Data with Javascript/103 Plotting MSE Values-en.srt 8KB
  280. 10 Natural Binary Classification/128 Refactoring with Cross Entropy-en.srt 8KB
  281. 13 Performance Optimization/156 Creating Memory Snapshots-en.srt 8KB
  282. 07 Increasing Performance with Vectorized Solutions/100 Recording MSE History-en.srt 8KB
  283. 09 Gradient Descent Alterations/106 Refactoring Towards Batch Gradient Descent-en.srt 8KB
  284. 04 Applications of Tensorflow/045 KNN with Regression-en.srt 8KB
  285. 02 Algorithm Overview/019 Gauging Accuracy-en.srt 8KB
  286. 12 Image Recognition In Action/145 Greyscale Values-en.srt 8KB
  287. 06 Gradient Descent with Tensorflow/073 Data Loading-en.srt 8KB
  288. 14 Appendix Custom CSV Loader/182 Extracting Data Columns-en.srt 8KB
  289. 03 Onwards to Tensorflow JS/035 A Plan to Move Forward-en.srt 8KB
  290. 11 Multi-Value Classification/135 Refactoring to Multi-Column Weights-en.srt 8KB
  291. 05 Getting Started with Gradient Descent/061 Why Linear Regression-en.srt 8KB
  292. 02 Algorithm Overview/021 Refactoring Accuracy Reporting-en.srt 8KB
  293. 01 What is Machine Learning/009 What Type of Problem-en.srt 8KB
  294. 11 Multi-Value Classification/141 Refactoring Sigmoid to Softmax-en.srt 8KB
  295. 13 Performance Optimization/155 Minimizing Memory Usage-en.srt 7KB
  296. 05 Getting Started with Gradient Descent/070 Gradient Descent with Multiple Terms-en.srt 7KB
  297. 10 Natural Binary Classification/114 The Sigmoid Equation-en.srt 7KB
  298. 11 Multi-Value Classification/136 A Problem to Test Multinominal Classification-en.srt 7KB
  299. 13 Performance Optimization/172 Fixing Cost History-en.srt 7KB
  300. 02 Algorithm Overview/014 Testing the Algorithm-en.srt 7KB
  301. 08 Plotting Data with Javascript/104 Plotting MSE History against B Values-en.srt 7KB
  302. 13 Performance Optimization/154 Handing Large Datasets-en.srt 7KB
  303. 11 Multi-Value Classification/137 Classifying Continuous Values-en.srt 7KB
  304. 07 Increasing Performance with Vectorized Solutions/092 Reminder on Standardization-en.srt 7KB
  305. 13 Performance Optimization/163 Tensorflows Eager Memory Usage-en.srt 7KB
  306. 02 Algorithm Overview/030 Applying Normalization-en.srt 7KB
  307. 13 Performance Optimization/171 NaN in Cost History-en.srt 7KB
  308. 10 Natural Binary Classification/120 Encoding Label Values-en.srt 7KB
  309. 10 Natural Binary Classification/129 Finishing the Cost Refactor-en.srt 7KB
  310. 08 Plotting Data with Javascript/102 Observing Changing Learning Rate and MSE-en.srt 7KB
  311. 10 Natural Binary Classification/122 The Sigmoid Equation with Logistic Regression-en.srt 7KB
  312. 13 Performance Optimization/174 Improving Model Accuracy-en.srt 7KB
  313. 01 What is Machine Learning/006 Identifying Relevant Data-en.srt 7KB
  314. 10 Natural Binary Classification/119 Importing Vehicle Data-en.srt 7KB
  315. 13 Performance Optimization/170 Plotting Cost History-en.srt 7KB
  316. 04 Applications of Tensorflow/046 A Change in Data Structure-en.srt 7KB
  317. 14 Appendix Custom CSV Loader/181 Custom Value Parsing-en.srt 7KB
  318. 02 Algorithm Overview/015 Interpreting Bad Results-en.srt 6KB
  319. 04 Applications of Tensorflow/059 What Now-en.srt 6KB
  320. 02 Algorithm Overview/024 Multi-Dimensional KNN-en.srt 6KB
  321. 13 Performance Optimization/166 Tidying the Training Loop-en.srt 6KB
  322. 03 Onwards to Tensorflow JS/039 Logging Tensor Data-en.srt 6KB
  323. 13 Performance Optimization/161 Measuring Footprint Reduction-en.srt 6KB
  324. 04 Applications of Tensorflow/057 Applying Standardization-en.srt 6KB
  325. 02 Algorithm Overview/016 Test and Training Data-en.srt 6KB
  326. 01 What is Machine Learning/008 Recording Observation Data-en.srt 6KB
  327. 05 Getting Started with Gradient Descent/069 Answering Common Questions-en.srt 6KB
  328. 11 Multi-Value Classification/133 A Smarter Refactor-en.srt 6KB
  329. 02 Algorithm Overview/017 Randomizing Test Data-en.srt 6KB
  330. 10 Natural Binary Classification/130 Plotting Changing Cost History-en.srt 6KB
  331. 02 Algorithm Overview/018 Generalizing KNN-en.srt 6KB
  332. 07 Increasing Performance with Vectorized Solutions/093 Data Processing in a Helper Method-en.srt 6KB
  333. 14 Appendix Custom CSV Loader/180 Parsing Number Values-en.srt 5KB
  334. 07 Increasing Performance with Vectorized Solutions/088 Same Results Or Not-en.srt 5KB
  335. 10 Natural Binary Classification/124 Gauging Classification Accuracy-en.srt 5KB
  336. 13 Performance Optimization/165 Implementing TF Tidy-en.srt 5KB
  337. 12 Image Recognition In Action/146 Many Features-en.srt 5KB
  338. 04 Applications of Tensorflow/051 Moving to the Editor-en.srt 5KB
  339. 11 Multi-Value Classification/143 Calculating Accuracy-en.srt 5KB
  340. 06 Gradient Descent with Tensorflow/075 Formulating the Training Loop-en.srt 5KB
  341. 02 Algorithm Overview/020 Printing a Report-en.srt 5KB
  342. 06 Gradient Descent with Tensorflow/078 Updating Coefficients-en.srt 5KB
  343. 13 Performance Optimization/160 Releasing References-en.srt 5KB
  344. 01 What is Machine Learning/005 Problem Outline-en.srt 5KB
  345. 07 Increasing Performance with Vectorized Solutions/096 Massaging Learning Rates-en.srt 5KB
  346. 05 Getting Started with Gradient Descent/060 Linear Regression-en.srt 4KB
  347. 13 Performance Optimization/169 Final Memory Report-en.srt 4KB
  348. 14 Appendix Custom CSV Loader/177 Reading Files from Disk-en.srt 4KB
  349. 13 Performance Optimization/164 Cleaning up Tensors with Tidy-en.srt 4KB
  350. 11 Multi-Value Classification/142 Implementing Accuracy Gauges-en.srt 4KB
  351. 14 Appendix Custom CSV Loader/178 Splitting into Columns-en.srt 4KB
  352. 02 Algorithm Overview/033 Evaluating Different Feature Values-en.srt 4KB
  353. 12 Image Recognition In Action/153 Backfilling Variance-en.srt 4KB
  354. 10 Natural Binary Classification/111 Introducing Logistic Regression-en.srt 4KB
  355. 14 Appendix Custom CSV Loader/179 Dropping Trailing Columns-en.srt 4KB
  356. 13 Performance Optimization/168 One More Optimization-en.srt 4KB
  357. 11 Multi-Value Classification/131 Multinominal Logistic Regression-en.srt 4KB
  358. 12 Image Recognition In Action/144 Handwriting Recognition-en.srt 4KB
  359. 01 What is Machine Learning/004 App Setup-en.srt 3KB
  360. 14 Appendix Custom CSV Loader/175 Loading CSV Files-en.srt 3KB
  361. 12 Image Recognition In Action/150 Unchanging Accuracy-en.srt 3KB
  362. 14 Appendix Custom CSV Loader/176 A Test Dataset-en.srt 3KB
  363. 13 Performance Optimization/173 Massaging Learning Parameters-en.srt 3KB
  364. 13 Performance Optimization/162 Optimization Tensorflow Memory Usage-en.srt 3KB
  365. 13 Performance Optimization/167 Measuring Reduced Memory Usage-en.srt 2KB
  366. 10 Natural Binary Classification/116 Changes for Logistic Regression-en.srt 2KB
  367. 01 What is Machine Learning/001 Getting Started - How to Get Help-en.srt 2KB
  368. 10 Natural Binary Classification/118 Project Download.html 1KB
  369. [FCS Forum].url 133B
  370. [FreeCourseSite.com].url 127B
  371. [CourseClub.NET].url 123B