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[] Udemy - Machine Learning with Javascript

  • 收录时间:2020-02-04 09:06:10
  • 文件大小:11GB
  • 下载次数:97
  • 最近下载:2020-12-27 12:34:02
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文件列表

  1. 5. Getting Started with Gradient Descent/9. Why a Learning Rate.mp4 187MB
  2. 6. Gradient Descent with Tensorflow/13. How it All Works Together!.mp4 144MB
  3. 2. Algorithm Overview/13. Investigating Optimal K Values.mp4 129MB
  4. 5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.mp4 127MB
  5. 5. Getting Started with Gradient Descent/12. Multiple Terms in Action.mp4 123MB
  6. 7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.mp4 121MB
  7. 5. Getting Started with Gradient Descent/7. Gradient Descent in Action.mp4 115MB
  8. 3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.mp4 114MB
  9. 1. What is Machine Learning/3. A Complete Walkthrough.mp4 109MB
  10. 2. Algorithm Overview/12. Refactoring Accuracy Reporting.srt 105MB
  11. 11. Multi-Value Classification/4. A Single Instance Approach.mp4 104MB
  12. 6. Gradient Descent with Tensorflow/8. Interpreting Results.mp4 102MB
  13. 13. Performance Optimization/6. Measuring Memory Usage.mp4 97MB
  14. 11. Multi-Value Classification/9. Marginal vs Conditional Probability.mp4 95MB
  15. 5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.mp4 93MB
  16. 2. Algorithm Overview/1. How K-Nearest Neighbor Works.mp4 93MB
  17. 4. Applications of Tensorflow/11. Normalization or Standardization.mp4 93MB
  18. 6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.mp4 91MB
  19. 4. Applications of Tensorflow/8. Loading CSV Data.mp4 89MB
  20. 12. Image Recognition In Action/8. Debugging the Calculation Process.mp4 89MB
  21. 6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.mp4 88MB
  22. 10. Natural Binary Classification/13. A Touch More Refactoring.mp4 87MB
  23. 4. Applications of Tensorflow/14. Debugging Calculations.mp4 87MB
  24. 7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.mp4 85MB
  25. 7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.mp4 82MB
  26. 7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.mp4 80MB
  27. 2. Algorithm Overview/22. Feature Selection with KNN.mp4 80MB
  28. 12. Image Recognition In Action/6. Implementing an Accuracy Gauge.mp4 80MB
  29. 9. Gradient Descent Alterations/6. Making Predictions with the Model.srt 80MB
  30. 9. Gradient Descent Alterations/6. Making Predictions with the Model.mp4 79MB
  31. 10. Natural Binary Classification/5. Decision Boundaries.mp4 79MB
  32. 2. Algorithm Overview/16. N-Dimension Distance.mp4 79MB
  33. 4. Applications of Tensorflow/3. KNN with Tensorflow.mp4 79MB
  34. 5. Getting Started with Gradient Descent/6. Derivatives!.mp4 78MB
  35. 9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.mp4 77MB
  36. 7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.mp4 77MB
  37. 3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.mp4 77MB
  38. 7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.mp4 76MB
  39. 14. Appendix Custom CSV Loader/10. Splitting Test and Training.mp4 76MB
  40. 2. Algorithm Overview/19. Feature Normalization.srt 73MB
  41. 2. Algorithm Overview/19. Feature Normalization.mp4 73MB
  42. 7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.mp4 73MB
  43. 7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.mp4 71MB
  44. 2. Algorithm Overview/17. Arbitrary Feature Spaces.mp4 71MB
  45. 2. Algorithm Overview/14. Updating KNN for Multiple Features.mp4 71MB
  46. 10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.mp4 70MB
  47. 10. Natural Binary Classification/16. Variable Decision Boundaries.mp4 68MB
  48. 6. Gradient Descent with Tensorflow/9. Matrix Multiplication.srt 67MB
  49. 6. Gradient Descent with Tensorflow/9. Matrix Multiplication.mp4 67MB
  50. 9. Gradient Descent Alterations/4. Iterating Over Batches.mp4 67MB
  51. 6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.mp4 67MB
  52. 2. Algorithm Overview/20. Normalization with MinMax.mp4 67MB
  53. 9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.mp4 66MB
  54. 7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.mp4 66MB
  55. 11. Multi-Value Classification/8. Training a Multinominal Model.mp4 66MB
  56. 9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.mp4 66MB
  57. 2. Algorithm Overview/23. Objective Feature Picking.mp4 66MB
  58. 5. Getting Started with Gradient Descent/8. Quick Breather and Review.mp4 66MB
  59. 2. Algorithm Overview/2. Lodash Review.mp4 65MB
  60. 4. Applications of Tensorflow/10. Reporting Error Percentages.mp4 64MB
  61. 2. Algorithm Overview/18. Magnitude Offsets in Features.mp4 64MB
  62. 6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.mp4 63MB
  63. 4. Applications of Tensorflow/5. Sorting Tensors.mp4 63MB
  64. 1. What is Machine Learning/2. Solving Machine Learning Problems.mp4 63MB
  65. 11. Multi-Value Classification/10. Sigmoid vs Softmax.mp4 63MB
  66. 6. Gradient Descent with Tensorflow/3. Default Algorithm Options.mp4 63MB
  67. 7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.mp4 62MB
  68. 3. Onwards to Tensorflow JS!/6. Broadcasting Operations.mp4 62MB
  69. 12. Image Recognition In Action/5. Encoding Label Values.mp4 62MB
  70. 8. Plotting Data with Javascript/2. Plotting MSE Values.mp4 61MB
  71. 10. Natural Binary Classification/2. Logistic Regression in Action.mp4 61MB
  72. 10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.mp4 60MB
  73. 6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.mp4 60MB
  74. 10. Natural Binary Classification/7. Project Setup for Logistic Regression.mp4 59MB
  75. 2. Algorithm Overview/3. Implementing KNN.mp4 59MB
  76. 3. Onwards to Tensorflow JS!/10. Creating Slices of Data.mp4 59MB
  77. 3. Onwards to Tensorflow JS!/5. Elementwise Operations.mp4 58MB
  78. 4. Applications of Tensorflow/6. Averaging Top Values.mp4 58MB
  79. 7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.mp4 58MB
  80. 12. Image Recognition In Action/4. Flattening Image Data.mp4 58MB
  81. 4. Applications of Tensorflow/4. Maintaining Order Relationships.mp4 58MB
  82. 14. Appendix Custom CSV Loader/8. Extracting Data Columns.mp4 57MB
  83. 6. Gradient Descent with Tensorflow/1. Project Overview.mp4 57MB
  84. 3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.mp4 57MB
  85. 13. Performance Optimization/5. Shallow vs Retained Memory Usage.mp4 57MB
  86. 5. Getting Started with Gradient Descent/5. Observations Around MSE.mp4 56MB
  87. 13. Performance Optimization/4. The Javascript Garbage Collector.mp4 56MB
  88. 10. Natural Binary Classification/3. Bad Equation Fits.mp4 55MB
  89. 12. Image Recognition In Action/2. Greyscale Values.mp4 55MB
  90. 9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.mp4 55MB
  91. 13. Performance Optimization/21. Improving Model Accuracy.mp4 55MB
  92. 4. Applications of Tensorflow/1. KNN with Regression.mp4 55MB
  93. 10. Natural Binary Classification/15. Implementing a Test Function.mp4 55MB
  94. 2. Algorithm Overview/10. Gauging Accuracy.mp4 54MB
  95. 4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.mp4 53MB
  96. 4. Applications of Tensorflow/9. Running an Analysis.mp4 53MB
  97. 2. Algorithm Overview/12. Refactoring Accuracy Reporting.mp4 52MB
  98. 14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.srt 52MB
  99. 14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.mp4 52MB
  100. 7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.mp4 52MB
  101. 5. Getting Started with Gradient Descent/2. Why Linear Regression.mp4 50MB
  102. 2. Algorithm Overview/4. Finishing KNN Implementation.mp4 50MB
  103. 11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.mp4 50MB
  104. 10. Natural Binary Classification/18. Refactoring with Cross Entropy.mp4 49MB
  105. 10. Natural Binary Classification/19. Finishing the Cost Refactor.mp4 49MB
  106. 13. Performance Optimization/3. Creating Memory Snapshots.mp4 49MB
  107. 11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.mp4 49MB
  108. 3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.mp4 49MB
  109. 10. Natural Binary Classification/10. Encoding Label Values.mp4 49MB
  110. 11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.mp4 48MB
  111. 11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.mp4 48MB
  112. 1. What is Machine Learning/7. Dataset Structures.mp4 48MB
  113. 12. Image Recognition In Action/9. Dealing with Zero Variances.mp4 48MB
  114. 7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.mp4 48MB
  115. 8. Plotting Data with Javascript/3. Plotting MSE History against B Values.mp4 48MB
  116. 13. Performance Optimization/17. Plotting Cost History.mp4 48MB
  117. 1. What is Machine Learning/9. What Type of Problem.mp4 47MB
  118. 13. Performance Optimization/10. Tensorflow's Eager Memory Usage.mp4 47MB
  119. 13. Performance Optimization/19. Fixing Cost History.mp4 47MB
  120. 13. Performance Optimization/18. NaN in Cost History.mp4 46MB
  121. 13. Performance Optimization/13. Tidying the Training Loop.mp4 46MB
  122. 8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.mp4 46MB
  123. 10. Natural Binary Classification/4. The Sigmoid Equation.mp4 45MB
  124. 2. Algorithm Overview/21. Applying Normalization.mp4 45MB
  125. 2. Algorithm Overview/7. Test and Training Data.mp4 45MB
  126. 2. Algorithm Overview/5. Testing the Algorithm.mp4 45MB
  127. 12. Image Recognition In Action/3. Many Features.mp4 45MB
  128. 11. Multi-Value Classification/7. Classifying Continuous Values.mp4 45MB
  129. 7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.mp4 44MB
  130. 13. Performance Optimization/1. Handing Large Datasets.mp4 44MB
  131. 2. Algorithm Overview/15. Multi-Dimensional KNN.mp4 44MB
  132. 5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.mp4 44MB
  133. 3. Onwards to Tensorflow JS!/11. Tensor Concatenation.mp4 44MB
  134. 6. Gradient Descent with Tensorflow/2. Data Loading.srt 44MB
  135. 6. Gradient Descent with Tensorflow/2. Data Loading.mp4 43MB
  136. 13. Performance Optimization/8. Measuring Footprint Reduction.mp4 43MB
  137. 10. Natural Binary Classification/20. Plotting Changing Cost History.mp4 43MB
  138. 4. Applications of Tensorflow/15. What Now.mp4 42MB
  139. 4. Applications of Tensorflow/13. Applying Standardization.mp4 41MB
  140. 3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.mp4 41MB
  141. 4. Applications of Tensorflow/2. A Change in Data Structure.srt 41MB
  142. 4. Applications of Tensorflow/2. A Change in Data Structure.mp4 41MB
  143. 5. Getting Started with Gradient Descent/10. Answering Common Questions.mp4 41MB
  144. 2. Algorithm Overview/6. Interpreting Bad Results.mp4 41MB
  145. 2. Algorithm Overview/9. Generalizing KNN.mp4 39MB
  146. 10. Natural Binary Classification/9. Importing Vehicle Data.mp4 39MB
  147. 11. Multi-Value Classification/3. A Smarter Refactor!.mp4 38MB
  148. 13. Performance Optimization/2. Minimizing Memory Usage.mp4 38MB
  149. 13. Performance Optimization/12. Implementing TF Tidy.mp4 38MB
  150. 7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.mp4 37MB
  151. 14. Appendix Custom CSV Loader/7. Custom Value Parsing.mp4 37MB
  152. 10. Natural Binary Classification/14. Gauging Classification Accuracy.mp4 37MB
  153. 7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.mp4 36MB
  154. 13. Performance Optimization/16. Final Memory Report.mp4 36MB
  155. 2. Algorithm Overview/8. Randomizing Test Data.mp4 36MB
  156. 13. Performance Optimization/7. Releasing References.mp4 36MB
  157. 4. Applications of Tensorflow/7. Moving to the Editor.srt 34MB
  158. 4. Applications of Tensorflow/7. Moving to the Editor.mp4 34MB
  159. 1. What is Machine Learning/6. Identifying Relevant Data.mp4 34MB
  160. 6. Gradient Descent with Tensorflow/7. Updating Coefficients.mp4 34MB
  161. 7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.mp4 34MB
  162. 2. Algorithm Overview/11. Printing a Report.mp4 33MB
  163. 10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.mp4 33MB
  164. 1. What is Machine Learning/8. Recording Observation Data.mp4 33MB
  165. 14. Appendix Custom CSV Loader/6. Parsing Number Values.mp4 31MB
  166. 11. Multi-Value Classification/13. Calculating Accuracy.mp4 31MB
  167. 1. What is Machine Learning/5. Problem Outline.mp4 31MB
  168. 3. Onwards to Tensorflow JS!/9. Tensor Accessors.mp4 30MB
  169. 11. Multi-Value Classification/12. Implementing Accuracy Gauges.mp4 29MB
  170. 2. Algorithm Overview/24. Evaluating Different Feature Values.mp4 28MB
  171. 4. Applications of Tensorflow/6. Averaging Top Values.srt 28MB
  172. 6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.srt 28MB
  173. 6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.mp4 28MB
  174. 13. Performance Optimization/15. One More Optimization.srt 27MB
  175. 13. Performance Optimization/15. One More Optimization.mp4 27MB
  176. 3. Onwards to Tensorflow JS!/8. Logging Tensor Data.mp4 26MB
  177. 12. Image Recognition In Action/10. Backfilling Variance.mp4 26MB
  178. 5. Getting Started with Gradient Descent/1. Linear Regression.mp4 25MB
  179. 11. Multi-Value Classification/1. Multinominal Logistic Regression.mp4 25MB
  180. 12. Image Recognition In Action/1. Handwriting Recognition.mp4 25MB
  181. 13. Performance Optimization/11. Cleaning up Tensors with Tidy.mp4 24MB
  182. 10. Natural Binary Classification/1. Introducing Logistic Regression.mp4 23MB
  183. 13. Performance Optimization/20. Massaging Learning Parameters.mp4 23MB
  184. 14. Appendix Custom CSV Loader/4. Splitting into Columns.mp4 20MB
  185. 12. Image Recognition In Action/7. Unchanging Accuracy.mp4 20MB
  186. 1. What is Machine Learning/4. App Setup.mp4 19MB
  187. 14. Appendix Custom CSV Loader/3. Reading Files from Disk.mp4 19MB
  188. 13. Performance Optimization/9. Optimization Tensorflow Memory Usage.mp4 19MB
  189. 14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.mp4 18MB
  190. 13. Performance Optimization/14. Measuring Reduced Memory Usage.mp4 18MB
  191. 14. Appendix Custom CSV Loader/1. Loading CSV Files.mp4 16MB
  192. 14. Appendix Custom CSV Loader/4. Splitting into Columns.srt 14MB
  193. 10. Natural Binary Classification/6. Changes for Logistic Regression.mp4 12MB
  194. 14. Appendix Custom CSV Loader/2. A Test Dataset.mp4 10MB
  195. 1. What is Machine Learning/1. Getting Started - How to Get Help.mp4 8MB
  196. 3. Onwards to Tensorflow JS!/6. Broadcasting Operations.srt 2MB
  197. 10. Natural Binary Classification/8.1 regressions.zip.zip 34KB
  198. 5. Getting Started with Gradient Descent/9. Why a Learning Rate.srt 26KB
  199. 6. Gradient Descent with Tensorflow/13. How it All Works Together!.srt 21KB
  200. 5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.srt 19KB
  201. 3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.srt 19KB
  202. 5. Getting Started with Gradient Descent/7. Gradient Descent in Action.srt 18KB
  203. 7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.srt 18KB
  204. 2. Algorithm Overview/13. Investigating Optimal K Values.srt 18KB
  205. 5. Getting Started with Gradient Descent/12. Multiple Terms in Action.srt 17KB
  206. 11. Multi-Value Classification/9. Marginal vs Conditional Probability.srt 16KB
  207. 6. Gradient Descent with Tensorflow/8. Interpreting Results.srt 15KB
  208. 5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.srt 15KB
  209. 11. Multi-Value Classification/4. A Single Instance Approach.srt 15KB
  210. 2. Algorithm Overview/16. N-Dimension Distance.srt 15KB
  211. 2. Algorithm Overview/2. Lodash Review.srt 15KB
  212. 1. What is Machine Learning/3. A Complete Walkthrough.srt 15KB
  213. 4. Applications of Tensorflow/8. Loading CSV Data.srt 15KB
  214. 4. Applications of Tensorflow/3. KNN with Tensorflow.srt 15KB
  215. 6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.srt 14KB
  216. 6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.srt 14KB
  217. 7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.srt 14KB
  218. 13. Performance Optimization/6. Measuring Memory Usage.srt 14KB
  219. 2. Algorithm Overview/17. Arbitrary Feature Spaces.srt 13KB
  220. 7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.srt 13KB
  221. 4. Applications of Tensorflow/14. Debugging Calculations.srt 13KB
  222. 2. Algorithm Overview/1. How K-Nearest Neighbor Works.srt 13KB
  223. 12. Image Recognition In Action/8. Debugging the Calculation Process.srt 13KB
  224. 2. Algorithm Overview/22. Feature Selection with KNN.srt 13KB
  225. 6. Gradient Descent with Tensorflow/3. Default Algorithm Options.srt 13KB
  226. 7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.srt 13KB
  227. 3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.srt 12KB
  228. 3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.srt 12KB
  229. 9. Gradient Descent Alterations/4. Iterating Over Batches.srt 12KB
  230. 4. Applications of Tensorflow/5. Sorting Tensors.srt 12KB
  231. 14. Appendix Custom CSV Loader/10. Splitting Test and Training.srt 12KB
  232. 7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.srt 12KB
  233. 10. Natural Binary Classification/5. Decision Boundaries.srt 12KB
  234. 3. Onwards to Tensorflow JS!/5. Elementwise Operations.srt 12KB
  235. 7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.srt 12KB
  236. 4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.srt 12KB
  237. 10. Natural Binary Classification/13. A Touch More Refactoring.srt 12KB
  238. 4. Applications of Tensorflow/11. Normalization or Standardization.srt 12KB
  239. 7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.srt 12KB
  240. 7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.srt 12KB
  241. 3. Onwards to Tensorflow JS!/10. Creating Slices of Data.srt 12KB
  242. 9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.srt 11KB
  243. 12. Image Recognition In Action/6. Implementing an Accuracy Gauge.srt 11KB
  244. 10. Natural Binary Classification/16. Variable Decision Boundaries.srt 11KB
  245. 10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.srt 11KB
  246. 5. Getting Started with Gradient Descent/6. Derivatives!.srt 11KB
  247. 10. Natural Binary Classification/2. Logistic Regression in Action.srt 11KB
  248. 4. Applications of Tensorflow/4. Maintaining Order Relationships.srt 11KB
  249. 2. Algorithm Overview/3. Implementing KNN.srt 11KB
  250. 2. Algorithm Overview/20. Normalization with MinMax.srt 10KB
  251. 2. Algorithm Overview/14. Updating KNN for Multiple Features.srt 10KB
  252. 13. Performance Optimization/4. The Javascript Garbage Collector.srt 10KB
  253. 7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.srt 10KB
  254. 7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.srt 10KB
  255. 12. Image Recognition In Action/9. Dealing with Zero Variances.srt 10KB
  256. 11. Multi-Value Classification/8. Training a Multinominal Model.srt 10KB
  257. 11. Multi-Value Classification/10. Sigmoid vs Softmax.srt 10KB
  258. 6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.srt 10KB
  259. 6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.srt 10KB
  260. 6. Gradient Descent with Tensorflow/1. Project Overview.srt 9KB
  261. 6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.srt 9KB
  262. 2. Algorithm Overview/23. Objective Feature Picking.srt 9KB
  263. 4. Applications of Tensorflow/9. Running an Analysis.srt 9KB
  264. 4. Applications of Tensorflow/10. Reporting Error Percentages.srt 9KB
  265. 5. Getting Started with Gradient Descent/5. Observations Around MSE.srt 9KB
  266. 1. What is Machine Learning/2. Solving Machine Learning Problems.srt 9KB
  267. 10. Natural Binary Classification/7. Project Setup for Logistic Regression.srt 9KB
  268. 1. What is Machine Learning/7. Dataset Structures.srt 9KB
  269. 5. Getting Started with Gradient Descent/8. Quick Breather and Review.srt 9KB
  270. 13. Performance Optimization/5. Shallow vs Retained Memory Usage.srt 9KB
  271. 9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.srt 9KB
  272. 7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.srt 9KB
  273. 10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.srt 9KB
  274. 12. Image Recognition In Action/4. Flattening Image Data.srt 9KB
  275. 2. Algorithm Overview/4. Finishing KNN Implementation.srt 9KB
  276. 9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.srt 9KB
  277. 2. Algorithm Overview/18. Magnitude Offsets in Features.srt 9KB
  278. 10. Natural Binary Classification/3. Bad Equation Fits.srt 9KB
  279. 10. Natural Binary Classification/15. Implementing a Test Function.srt 9KB
  280. 3. Onwards to Tensorflow JS!/9. Tensor Accessors.srt 9KB
  281. 3. Onwards to Tensorflow JS!/11. Tensor Concatenation.srt 9KB
  282. 7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.srt 9KB
  283. 12. Image Recognition In Action/5. Encoding Label Values.srt 8KB
  284. 3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.srt 8KB
  285. 11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.srt 8KB
  286. 8. Plotting Data with Javascript/2. Plotting MSE Values.srt 8KB
  287. 10. Natural Binary Classification/18. Refactoring with Cross Entropy.srt 8KB
  288. 13. Performance Optimization/3. Creating Memory Snapshots.srt 8KB
  289. 7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.srt 8KB
  290. 9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.srt 8KB
  291. 4. Applications of Tensorflow/1. KNN with Regression.srt 8KB
  292. 2. Algorithm Overview/10. Gauging Accuracy.srt 8KB
  293. 12. Image Recognition In Action/2. Greyscale Values.srt 8KB
  294. 14. Appendix Custom CSV Loader/8. Extracting Data Columns.srt 8KB
  295. 3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.srt 8KB
  296. 11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.srt 8KB
  297. 5. Getting Started with Gradient Descent/2. Why Linear Regression.srt 8KB
  298. 1. What is Machine Learning/9. What Type of Problem.srt 8KB
  299. 11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.srt 8KB
  300. 13. Performance Optimization/2. Minimizing Memory Usage.srt 7KB
  301. 5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.srt 7KB
  302. 10. Natural Binary Classification/4. The Sigmoid Equation.srt 7KB
  303. 11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.srt 7KB
  304. 13. Performance Optimization/19. Fixing Cost History.srt 7KB
  305. 2. Algorithm Overview/5. Testing the Algorithm.srt 7KB
  306. 8. Plotting Data with Javascript/3. Plotting MSE History against B Values.srt 7KB
  307. 13. Performance Optimization/1. Handing Large Datasets.srt 7KB
  308. 11. Multi-Value Classification/7. Classifying Continuous Values.srt 7KB
  309. 7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.srt 7KB
  310. 13. Performance Optimization/10. Tensorflow's Eager Memory Usage.srt 7KB
  311. 2. Algorithm Overview/21. Applying Normalization.srt 7KB
  312. 13. Performance Optimization/18. NaN in Cost History.srt 7KB
  313. 10. Natural Binary Classification/10. Encoding Label Values.srt 7KB
  314. 10. Natural Binary Classification/19. Finishing the Cost Refactor.srt 7KB
  315. 8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.srt 7KB
  316. 10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.srt 7KB
  317. 13. Performance Optimization/21. Improving Model Accuracy.srt 7KB
  318. 1. What is Machine Learning/6. Identifying Relevant Data.srt 7KB
  319. 10. Natural Binary Classification/9. Importing Vehicle Data.srt 7KB
  320. 13. Performance Optimization/17. Plotting Cost History.srt 7KB
  321. 14. Appendix Custom CSV Loader/7. Custom Value Parsing.srt 7KB
  322. 2. Algorithm Overview/6. Interpreting Bad Results.srt 6KB
  323. 4. Applications of Tensorflow/15. What Now.srt 6KB
  324. 2. Algorithm Overview/15. Multi-Dimensional KNN.srt 6KB
  325. 13. Performance Optimization/13. Tidying the Training Loop.srt 6KB
  326. 3. Onwards to Tensorflow JS!/8. Logging Tensor Data.srt 6KB
  327. 13. Performance Optimization/8. Measuring Footprint Reduction.srt 6KB
  328. 4. Applications of Tensorflow/13. Applying Standardization.srt 6KB
  329. 2. Algorithm Overview/7. Test and Training Data.srt 6KB
  330. 1. What is Machine Learning/8. Recording Observation Data.srt 6KB
  331. 5. Getting Started with Gradient Descent/10. Answering Common Questions.srt 6KB
  332. 11. Multi-Value Classification/3. A Smarter Refactor!.srt 6KB
  333. 10. Natural Binary Classification/20. Plotting Changing Cost History.srt 6KB
  334. 2. Algorithm Overview/8. Randomizing Test Data.srt 6KB
  335. 2. Algorithm Overview/9. Generalizing KNN.srt 6KB
  336. 7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.srt 6KB
  337. 14. Appendix Custom CSV Loader/6. Parsing Number Values.srt 5KB
  338. 7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.srt 5KB
  339. 10. Natural Binary Classification/14. Gauging Classification Accuracy.srt 5KB
  340. 13. Performance Optimization/12. Implementing TF Tidy.srt 5KB
  341. 12. Image Recognition In Action/3. Many Features.srt 5KB
  342. 11. Multi-Value Classification/13. Calculating Accuracy.srt 5KB
  343. 2. Algorithm Overview/11. Printing a Report.srt 5KB
  344. 6. Gradient Descent with Tensorflow/7. Updating Coefficients.srt 5KB
  345. 13. Performance Optimization/7. Releasing References.srt 5KB
  346. 1. What is Machine Learning/5. Problem Outline.srt 5KB
  347. 7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.srt 5KB
  348. 5. Getting Started with Gradient Descent/1. Linear Regression.srt 4KB
  349. 13. Performance Optimization/16. Final Memory Report.srt 4KB
  350. 14. Appendix Custom CSV Loader/3. Reading Files from Disk.srt 4KB
  351. 13. Performance Optimization/11. Cleaning up Tensors with Tidy.srt 4KB
  352. 11. Multi-Value Classification/12. Implementing Accuracy Gauges.srt 4KB
  353. 2. Algorithm Overview/24. Evaluating Different Feature Values.srt 4KB
  354. 12. Image Recognition In Action/10. Backfilling Variance.srt 4KB
  355. 10. Natural Binary Classification/1. Introducing Logistic Regression.srt 4KB
  356. 14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.srt 4KB
  357. 11. Multi-Value Classification/1. Multinominal Logistic Regression.srt 4KB
  358. 12. Image Recognition In Action/1. Handwriting Recognition.srt 4KB
  359. 1. What is Machine Learning/4. App Setup.srt 3KB
  360. 14. Appendix Custom CSV Loader/1. Loading CSV Files.srt 3KB
  361. 12. Image Recognition In Action/7. Unchanging Accuracy.srt 3KB
  362. 14. Appendix Custom CSV Loader/2. A Test Dataset.srt 3KB
  363. 13. Performance Optimization/20. Massaging Learning Parameters.srt 3KB
  364. 13. Performance Optimization/9. Optimization Tensorflow Memory Usage.srt 3KB
  365. 13. Performance Optimization/14. Measuring Reduced Memory Usage.srt 2KB
  366. 15. Extras/1. Bonus!.html 2KB
  367. 10. Natural Binary Classification/6. Changes for Logistic Regression.srt 2KB
  368. 1. What is Machine Learning/1. Getting Started - How to Get Help.srt 2KB
  369. 10. Natural Binary Classification/8. Project Download.html 215B
  370. 3. Onwards to Tensorflow JS!/4. Tensor Dimension and Shapes.html 143B
  371. 3. Onwards to Tensorflow JS!/7. Broadcasting Elementwise Operations.html 143B
  372. 0. Websites you may like/[FCS Forum].url 133B
  373. 0. Websites you may like/[FreeCourseSite.com].url 127B
  374. 0. Websites you may like/[CourseClub.ME].url 122B