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[] Udemy - Machine Learning with Javascript

  • 收录时间:2021-01-09 23:38:56
  • 文件大小:10GB
  • 下载次数:5
  • 最近下载:2021-01-18 20:07:04
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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. 11. Multi-Value Classification/4. A Single Instance Approach.mp4 104MB
  11. 6. Gradient Descent with Tensorflow/8. Interpreting Results.mp4 102MB
  12. 13. Performance Optimization/6. Measuring Memory Usage.mp4 97MB
  13. 11. Multi-Value Classification/9. Marginal vs Conditional Probability.mp4 95MB
  14. 5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.mp4 93MB
  15. 2. Algorithm Overview/1. How K-Nearest Neighbor Works.mp4 93MB
  16. 4. Applications of Tensorflow/11. Normalization or Standardization.mp4 93MB
  17. 6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.mp4 91MB
  18. 4. Applications of Tensorflow/8. Loading CSV Data.mp4 89MB
  19. 12. Image Recognition In Action/8. Debugging the Calculation Process.mp4 89MB
  20. 6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.mp4 88MB
  21. 10. Natural Binary Classification/13. A Touch More Refactoring.mp4 87MB
  22. 4. Applications of Tensorflow/14. Debugging Calculations.mp4 87MB
  23. 7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.mp4 85MB
  24. 7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.mp4 82MB
  25. 7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.mp4 80MB
  26. 2. Algorithm Overview/22. Feature Selection with KNN.mp4 80MB
  27. 12. Image Recognition In Action/6. Implementing an Accuracy Gauge.mp4 80MB
  28. 9. Gradient Descent Alterations/6. Making Predictions with the Model.mp4 79MB
  29. 10. Natural Binary Classification/5. Decision Boundaries.mp4 79MB
  30. 2. Algorithm Overview/16. N-Dimension Distance.mp4 79MB
  31. 4. Applications of Tensorflow/3. KNN with Tensorflow.mp4 79MB
  32. 5. Getting Started with Gradient Descent/6. Derivatives!.mp4 78MB
  33. 9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.mp4 77MB
  34. 7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.mp4 77MB
  35. 3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.mp4 77MB
  36. 7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.mp4 76MB
  37. 14. Appendix Custom CSV Loader/10. Splitting Test and Training.mp4 76MB
  38. 2. Algorithm Overview/19. Feature Normalization.mp4 73MB
  39. 7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.mp4 73MB
  40. 7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.mp4 71MB
  41. 2. Algorithm Overview/17. Arbitrary Feature Spaces.mp4 71MB
  42. 2. Algorithm Overview/14. Updating KNN for Multiple Features.mp4 71MB
  43. 10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.mp4 70MB
  44. 10. Natural Binary Classification/16. Variable Decision Boundaries.mp4 68MB
  45. 6. Gradient Descent with Tensorflow/9. Matrix Multiplication.mp4 67MB
  46. 9. Gradient Descent Alterations/4. Iterating Over Batches.mp4 67MB
  47. 6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.mp4 67MB
  48. 2. Algorithm Overview/20. Normalization with MinMax.mp4 67MB
  49. 9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.mp4 66MB
  50. 7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.mp4 66MB
  51. 11. Multi-Value Classification/8. Training a Multinominal Model.mp4 66MB
  52. 9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.mp4 66MB
  53. 2. Algorithm Overview/23. Objective Feature Picking.mp4 66MB
  54. 5. Getting Started with Gradient Descent/8. Quick Breather and Review.mp4 66MB
  55. 2. Algorithm Overview/2. Lodash Review.mp4 65MB
  56. 4. Applications of Tensorflow/10. Reporting Error Percentages.mp4 65MB
  57. 2. Algorithm Overview/18. Magnitude Offsets in Features.mp4 64MB
  58. 6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.mp4 63MB
  59. 4. Applications of Tensorflow/5. Sorting Tensors.mp4 63MB
  60. 1. What is Machine Learning/2. Solving Machine Learning Problems.mp4 63MB
  61. 11. Multi-Value Classification/10. Sigmoid vs Softmax.mp4 63MB
  62. 6. Gradient Descent with Tensorflow/3. Default Algorithm Options.vtt 63MB
  63. 6. Gradient Descent with Tensorflow/3. Default Algorithm Options.mp4 63MB
  64. 7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.mp4 62MB
  65. 3. Onwards to Tensorflow JS!/6. Broadcasting Operations.mp4 62MB
  66. 12. Image Recognition In Action/5. Encoding Label Values.mp4 62MB
  67. 8. Plotting Data with Javascript/2. Plotting MSE Values.mp4 61MB
  68. 10. Natural Binary Classification/2. Logistic Regression in Action.mp4 61MB
  69. 10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.mp4 60MB
  70. 6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.mp4 60MB
  71. 10. Natural Binary Classification/7. Project Setup for Logistic Regression.mp4 59MB
  72. 2. Algorithm Overview/3. Implementing KNN.mp4 59MB
  73. 3. Onwards to Tensorflow JS!/10. Creating Slices of Data.mp4 59MB
  74. 3. Onwards to Tensorflow JS!/5. Elementwise Operations.mp4 58MB
  75. 4. Applications of Tensorflow/6. Averaging Top Values.mp4 58MB
  76. 7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.mp4 58MB
  77. 12. Image Recognition In Action/4. Flattening Image Data.mp4 58MB
  78. 4. Applications of Tensorflow/4. Maintaining Order Relationships.mp4 58MB
  79. 14. Appendix Custom CSV Loader/8. Extracting Data Columns.mp4 57MB
  80. 6. Gradient Descent with Tensorflow/1. Project Overview.mp4 57MB
  81. 3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.mp4 57MB
  82. 13. Performance Optimization/5. Shallow vs Retained Memory Usage.mp4 57MB
  83. 5. Getting Started with Gradient Descent/5. Observations Around MSE.mp4 56MB
  84. 13. Performance Optimization/4. The Javascript Garbage Collector.mp4 56MB
  85. 10. Natural Binary Classification/3. Bad Equation Fits.mp4 55MB
  86. 12. Image Recognition In Action/2. Greyscale Values.mp4 55MB
  87. 9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.mp4 55MB
  88. 13. Performance Optimization/21. Improving Model Accuracy.mp4 55MB
  89. 4. Applications of Tensorflow/1. KNN with Regression.mp4 55MB
  90. 10. Natural Binary Classification/15. Implementing a Test Function.mp4 55MB
  91. 2. Algorithm Overview/10. Gauging Accuracy.mp4 54MB
  92. 4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.mp4 53MB
  93. 4. Applications of Tensorflow/9. Running an Analysis.mp4 53MB
  94. 2. Algorithm Overview/12. Refactoring Accuracy Reporting.mp4 52MB
  95. 14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.mp4 52MB
  96. 7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.mp4 52MB
  97. 5. Getting Started with Gradient Descent/2. Why Linear Regression.mp4 50MB
  98. 2. Algorithm Overview/4. Finishing KNN Implementation.mp4 50MB
  99. 11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.mp4 50MB
  100. 10. Natural Binary Classification/18. Refactoring with Cross Entropy.mp4 49MB
  101. 10. Natural Binary Classification/19. Finishing the Cost Refactor.mp4 49MB
  102. 13. Performance Optimization/3. Creating Memory Snapshots.mp4 49MB
  103. 11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.mp4 49MB
  104. 3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.mp4 49MB
  105. 10. Natural Binary Classification/10. Encoding Label Values.mp4 49MB
  106. 11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.mp4 48MB
  107. 11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.mp4 48MB
  108. 1. What is Machine Learning/7. Dataset Structures.mp4 48MB
  109. 12. Image Recognition In Action/9. Dealing with Zero Variances.mp4 48MB
  110. 7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.mp4 48MB
  111. 8. Plotting Data with Javascript/3. Plotting MSE History against B Values.mp4 48MB
  112. 13. Performance Optimization/17. Plotting Cost History.mp4 48MB
  113. 1. What is Machine Learning/9. What Type of Problem.mp4 47MB
  114. 13. Performance Optimization/10. Tensorflow's Eager Memory Usage.mp4 47MB
  115. 13. Performance Optimization/19. Fixing Cost History.mp4 47MB
  116. 13. Performance Optimization/18. NaN in Cost History.mp4 46MB
  117. 13. Performance Optimization/13. Tidying the Training Loop.mp4 46MB
  118. 8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.mp4 46MB
  119. 10. Natural Binary Classification/4. The Sigmoid Equation.mp4 45MB
  120. 2. Algorithm Overview/21. Applying Normalization.mp4 45MB
  121. 2. Algorithm Overview/7. Test and Training Data.mp4 45MB
  122. 2. Algorithm Overview/5. Testing the Algorithm.mp4 45MB
  123. 12. Image Recognition In Action/3. Many Features.mp4 45MB
  124. 11. Multi-Value Classification/7. Classifying Continuous Values.mp4 45MB
  125. 7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.mp4 44MB
  126. 13. Performance Optimization/1. Handing Large Datasets.mp4 44MB
  127. 5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.mp4 44MB
  128. 2. Algorithm Overview/15. Multi-Dimensional KNN.mp4 44MB
  129. 3. Onwards to Tensorflow JS!/11. Tensor Concatenation.mp4 44MB
  130. 6. Gradient Descent with Tensorflow/2. Data Loading.mp4 43MB
  131. 13. Performance Optimization/8. Measuring Footprint Reduction.mp4 43MB
  132. 10. Natural Binary Classification/20. Plotting Changing Cost History.mp4 43MB
  133. 4. Applications of Tensorflow/15. What Now.mp4 42MB
  134. 3. Onwards to Tensorflow JS!/2. A Plan to Move Forward.vtt 42MB
  135. 4. Applications of Tensorflow/13. Applying Standardization.mp4 41MB
  136. 3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.mp4 41MB
  137. 4. Applications of Tensorflow/2. A Change in Data Structure.mp4 41MB
  138. 5. Getting Started with Gradient Descent/10. Answering Common Questions.mp4 41MB
  139. 2. Algorithm Overview/6. Interpreting Bad Results.mp4 41MB
  140. 2. Algorithm Overview/9. Generalizing KNN.mp4 39MB
  141. 10. Natural Binary Classification/9. Importing Vehicle Data.mp4 39MB
  142. 11. Multi-Value Classification/3. A Smarter Refactor!.mp4 38MB
  143. 13. Performance Optimization/2. Minimizing Memory Usage.mp4 38MB
  144. 13. Performance Optimization/12. Implementing TF Tidy.mp4 38MB
  145. 7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.mp4 37MB
  146. 14. Appendix Custom CSV Loader/7. Custom Value Parsing.mp4 37MB
  147. 10. Natural Binary Classification/14. Gauging Classification Accuracy.mp4 37MB
  148. 7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.mp4 36MB
  149. 13. Performance Optimization/16. Final Memory Report.mp4 36MB
  150. 2. Algorithm Overview/8. Randomizing Test Data.mp4 36MB
  151. 13. Performance Optimization/7. Releasing References.mp4 36MB
  152. 4. Applications of Tensorflow/7. Moving to the Editor.mp4 34MB
  153. 1. What is Machine Learning/6. Identifying Relevant Data.mp4 34MB
  154. 6. Gradient Descent with Tensorflow/7. Updating Coefficients.mp4 34MB
  155. 7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.mp4 34MB
  156. 2. Algorithm Overview/11. Printing a Report.mp4 33MB
  157. 10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.mp4 33MB
  158. 1. What is Machine Learning/8. Recording Observation Data.mp4 33MB
  159. 14. Appendix Custom CSV Loader/6. Parsing Number Values.mp4 31MB
  160. 11. Multi-Value Classification/13. Calculating Accuracy.mp4 31MB
  161. 1. What is Machine Learning/5. Problem Outline.mp4 31MB
  162. 3. Onwards to Tensorflow JS!/9. Tensor Accessors.mp4 30MB
  163. 11. Multi-Value Classification/12. Implementing Accuracy Gauges.mp4 29MB
  164. 2. Algorithm Overview/24. Evaluating Different Feature Values.mp4 28MB
  165. 6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.mp4 28MB
  166. 13. Performance Optimization/15. One More Optimization.mp4 27MB
  167. 3. Onwards to Tensorflow JS!/8. Logging Tensor Data.mp4 26MB
  168. 12. Image Recognition In Action/10. Backfilling Variance.mp4 26MB
  169. 5. Getting Started with Gradient Descent/1. Linear Regression.mp4 25MB
  170. 11. Multi-Value Classification/1. Multinominal Logistic Regression.mp4 25MB
  171. 12. Image Recognition In Action/1. Handwriting Recognition.mp4 25MB
  172. 13. Performance Optimization/11. Cleaning up Tensors with Tidy.mp4 24MB
  173. 10. Natural Binary Classification/1. Introducing Logistic Regression.mp4 23MB
  174. 13. Performance Optimization/20. Massaging Learning Parameters.mp4 23MB
  175. 14. Appendix Custom CSV Loader/4. Splitting into Columns.mp4 20MB
  176. 12. Image Recognition In Action/7. Unchanging Accuracy.mp4 20MB
  177. 1. What is Machine Learning/4. App Setup.mp4 19MB
  178. 14. Appendix Custom CSV Loader/3. Reading Files from Disk.mp4 19MB
  179. 13. Performance Optimization/9. Optimization Tensorflow Memory Usage.mp4 19MB
  180. 14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.mp4 18MB
  181. 13. Performance Optimization/14. Measuring Reduced Memory Usage.mp4 18MB
  182. 14. Appendix Custom CSV Loader/1. Loading CSV Files.mp4 16MB
  183. 10. Natural Binary Classification/6. Changes for Logistic Regression.mp4 12MB
  184. 14. Appendix Custom CSV Loader/2. A Test Dataset.mp4 10MB
  185. 1. What is Machine Learning/1. Getting Started - How to Get Help.mp4 8MB
  186. 10. Natural Binary Classification/8.1 regressions.zip.zip 34KB
  187. 5. Getting Started with Gradient Descent/9. Why a Learning Rate.vtt 23KB
  188. 6. Gradient Descent with Tensorflow/13. How it All Works Together!.vtt 18KB
  189. 5. Getting Started with Gradient Descent/3. Understanding Gradient Descent.vtt 17KB
  190. 3. Onwards to Tensorflow JS!/3. Tensor Shape and Dimension.vtt 17KB
  191. 5. Getting Started with Gradient Descent/7. Gradient Descent in Action.vtt 16KB
  192. 7. Increasing Performance with Vectorized Solutions/13. Moving Towards Multivariate Regression.vtt 16KB
  193. 2. Algorithm Overview/13. Investigating Optimal K Values.vtt 16KB
  194. 5. Getting Started with Gradient Descent/12. Multiple Terms in Action.vtt 14KB
  195. 11. Multi-Value Classification/9. Marginal vs Conditional Probability.vtt 14KB
  196. 6. Gradient Descent with Tensorflow/8. Interpreting Results.vtt 14KB
  197. 5. Getting Started with Gradient Descent/4. Guessing Coefficients with MSE.vtt 14KB
  198. 11. Multi-Value Classification/4. A Single Instance Approach.vtt 13KB
  199. 2. Algorithm Overview/2. Lodash Review.vtt 13KB
  200. 2. Algorithm Overview/16. N-Dimension Distance.vtt 13KB
  201. 1. What is Machine Learning/3. A Complete Walkthrough.vtt 13KB
  202. 4. Applications of Tensorflow/8. Loading CSV Data.vtt 13KB
  203. 4. Applications of Tensorflow/3. KNN with Tensorflow.vtt 13KB
  204. 6. Gradient Descent with Tensorflow/12. Simplification with Matrix Multiplication.vtt 13KB
  205. 6. Gradient Descent with Tensorflow/5. Initial Gradient Descent Implementation.vtt 12KB
  206. 7. Increasing Performance with Vectorized Solutions/2. Refactoring to One Equation.vtt 12KB
  207. 13. Performance Optimization/6. Measuring Memory Usage.vtt 12KB
  208. 2. Algorithm Overview/17. Arbitrary Feature Spaces.vtt 12KB
  209. 7. Increasing Performance with Vectorized Solutions/5. Calculating Model Accuracy.vtt 12KB
  210. 4. Applications of Tensorflow/14. Debugging Calculations.vtt 11KB
  211. 2. Algorithm Overview/1. How K-Nearest Neighbor Works.vtt 11KB
  212. 12. Image Recognition In Action/8. Debugging the Calculation Process.vtt 11KB
  213. 2. Algorithm Overview/22. Feature Selection with KNN.vtt 11KB
  214. 7. Increasing Performance with Vectorized Solutions/15. Learning Rate Optimization.vtt 11KB
  215. 3. Onwards to Tensorflow JS!/1. Let's Get Our Bearings.vtt 11KB
  216. 3. Onwards to Tensorflow JS!/13. Massaging Dimensions with ExpandDims.vtt 11KB
  217. 4. Applications of Tensorflow/5. Sorting Tensors.vtt 11KB
  218. 10. Natural Binary Classification/5. Decision Boundaries.vtt 11KB
  219. 9. Gradient Descent Alterations/4. Iterating Over Batches.vtt 11KB
  220. 7. Increasing Performance with Vectorized Solutions/14. Refactoring for Multivariate Analysis.vtt 10KB
  221. 9. Gradient Descent Alterations/6. Making Predictions with the Model.vtt 10KB
  222. 3. Onwards to Tensorflow JS!/5. Elementwise Operations.vtt 10KB
  223. 14. Appendix Custom CSV Loader/10. Splitting Test and Training.vtt 10KB
  224. 7. Increasing Performance with Vectorized Solutions/7. Dealing with Bad Accuracy.vtt 10KB
  225. 10. Natural Binary Classification/13. A Touch More Refactoring.vtt 10KB
  226. 4. Applications of Tensorflow/12. Numerical Standardization with Tensorflow.vtt 10KB
  227. 7. Increasing Performance with Vectorized Solutions/6. Implementing Coefficient of Determination.vtt 10KB
  228. 4. Applications of Tensorflow/11. Normalization or Standardization.vtt 10KB
  229. 4. Applications of Tensorflow/6. Averaging Top Values.vtt 10KB
  230. 7. Increasing Performance with Vectorized Solutions/1. Refactoring the Linear Regression Class.vtt 10KB
  231. 3. Onwards to Tensorflow JS!/10. Creating Slices of Data.vtt 10KB
  232. 2. Algorithm Overview/19. Feature Normalization.vtt 10KB
  233. 9. Gradient Descent Alterations/1. Batch and Stochastic Gradient Descent.vtt 10KB
  234. 10. Natural Binary Classification/16. Variable Decision Boundaries.vtt 10KB
  235. 12. Image Recognition In Action/6. Implementing an Accuracy Gauge.vtt 10KB
  236. 6. Gradient Descent with Tensorflow/9. Matrix Multiplication.vtt 10KB
  237. 10. Natural Binary Classification/11. Updating Linear Regression for Logistic Regression.vtt 10KB
  238. 5. Getting Started with Gradient Descent/6. Derivatives!.vtt 10KB
  239. 10. Natural Binary Classification/2. Logistic Regression in Action.vtt 9KB
  240. 3. Onwards to Tensorflow JS!/6. Broadcasting Operations.vtt 9KB
  241. 4. Applications of Tensorflow/4. Maintaining Order Relationships.vtt 9KB
  242. 2. Algorithm Overview/3. Implementing KNN.vtt 9KB
  243. 2. Algorithm Overview/20. Normalization with MinMax.vtt 9KB
  244. 2. Algorithm Overview/14. Updating KNN for Multiple Features.vtt 9KB
  245. 13. Performance Optimization/4. The Javascript Garbage Collector.vtt 9KB
  246. 7. Increasing Performance with Vectorized Solutions/17. Updating Learning Rate.vtt 9KB
  247. 7. Increasing Performance with Vectorized Solutions/3. A Few More Changes.vtt 9KB
  248. 12. Image Recognition In Action/9. Dealing with Zero Variances.vtt 9KB
  249. 11. Multi-Value Classification/10. Sigmoid vs Softmax.vtt 9KB
  250. 11. Multi-Value Classification/8. Training a Multinominal Model.vtt 9KB
  251. 6. Gradient Descent with Tensorflow/11. Matrix Form of Slope Equations.vtt 8KB
  252. 6. Gradient Descent with Tensorflow/6. Calculating MSE Slopes.vtt 8KB
  253. 6. Gradient Descent with Tensorflow/1. Project Overview.vtt 8KB
  254. 6. Gradient Descent with Tensorflow/10. More on Matrix Multiplication.vtt 8KB
  255. 2. Algorithm Overview/23. Objective Feature Picking.vtt 8KB
  256. 4. Applications of Tensorflow/10. Reporting Error Percentages.vtt 8KB
  257. 1. What is Machine Learning/2. Solving Machine Learning Problems.vtt 8KB
  258. 5. Getting Started with Gradient Descent/5. Observations Around MSE.vtt 8KB
  259. 4. Applications of Tensorflow/9. Running an Analysis.vtt 8KB
  260. 10. Natural Binary Classification/7. Project Setup for Logistic Regression.vtt 8KB
  261. 1. What is Machine Learning/7. Dataset Structures.vtt 8KB
  262. 13. Performance Optimization/5. Shallow vs Retained Memory Usage.vtt 8KB
  263. 5. Getting Started with Gradient Descent/8. Quick Breather and Review.vtt 8KB
  264. 9. Gradient Descent Alterations/5. Evaluating Batch Gradient Descent Results.vtt 8KB
  265. 7. Increasing Performance with Vectorized Solutions/11. Fixing Standardization Issues.vtt 8KB
  266. 10. Natural Binary Classification/17. Mean Squared Error vs Cross Entropy.vtt 8KB
  267. 9. Gradient Descent Alterations/3. Determining Batch Size and Quantity.vtt 8KB
  268. 12. Image Recognition In Action/4. Flattening Image Data.vtt 8KB
  269. 2. Algorithm Overview/4. Finishing KNN Implementation.vtt 8KB
  270. 10. Natural Binary Classification/3. Bad Equation Fits.vtt 8KB
  271. 2. Algorithm Overview/18. Magnitude Offsets in Features.vtt 8KB
  272. 7. Increasing Performance with Vectorized Solutions/10. Reapplying Standardization.vtt 8KB
  273. 3. Onwards to Tensorflow JS!/9. Tensor Accessors.vtt 7KB
  274. 10. Natural Binary Classification/15. Implementing a Test Function.vtt 7KB
  275. 3. Onwards to Tensorflow JS!/11. Tensor Concatenation.vtt 7KB
  276. 12. Image Recognition In Action/5. Encoding Label Values.vtt 7KB
  277. 14. Appendix Custom CSV Loader/9. Shuffling Data via Seed Phrase.vtt 7KB
  278. 3. Onwards to Tensorflow JS!/12. Summing Values Along an Axis.vtt 7KB
  279. 11. Multi-Value Classification/2. A Smart Refactor to Multinominal Analysis.vtt 7KB
  280. 8. Plotting Data with Javascript/2. Plotting MSE Values.vtt 7KB
  281. 10. Natural Binary Classification/18. Refactoring with Cross Entropy.vtt 7KB
  282. 13. Performance Optimization/3. Creating Memory Snapshots.vtt 7KB
  283. 7. Increasing Performance with Vectorized Solutions/16. Recording MSE History.vtt 7KB
  284. 9. Gradient Descent Alterations/2. Refactoring Towards Batch Gradient Descent.vtt 7KB
  285. 4. Applications of Tensorflow/1. KNN with Regression.vtt 7KB
  286. 12. Image Recognition In Action/2. Greyscale Values.vtt 7KB
  287. 2. Algorithm Overview/10. Gauging Accuracy.vtt 7KB
  288. 6. Gradient Descent with Tensorflow/2. Data Loading.vtt 7KB
  289. 14. Appendix Custom CSV Loader/8. Extracting Data Columns.vtt 7KB
  290. 5. Getting Started with Gradient Descent/2. Why Linear Regression.vtt 7KB
  291. 1. What is Machine Learning/9. What Type of Problem.vtt 7KB
  292. 2. Algorithm Overview/12. Refactoring Accuracy Reporting.vtt 7KB
  293. 11. Multi-Value Classification/5. Refactoring to Multi-Column Weights.vtt 7KB
  294. 13. Performance Optimization/2. Minimizing Memory Usage.vtt 7KB
  295. 5. Getting Started with Gradient Descent/11. Gradient Descent with Multiple Terms.vtt 7KB
  296. 11. Multi-Value Classification/11. Refactoring Sigmoid to Softmax.vtt 7KB
  297. 10. Natural Binary Classification/4. The Sigmoid Equation.vtt 6KB
  298. 11. Multi-Value Classification/6. A Problem to Test Multinominal Classification.vtt 6KB
  299. 13. Performance Optimization/19. Fixing Cost History.vtt 6KB
  300. 2. Algorithm Overview/5. Testing the Algorithm.vtt 6KB
  301. 8. Plotting Data with Javascript/3. Plotting MSE History against B Values.vtt 6KB
  302. 11. Multi-Value Classification/7. Classifying Continuous Values.vtt 6KB
  303. 13. Performance Optimization/1. Handing Large Datasets.vtt 6KB
  304. 7. Increasing Performance with Vectorized Solutions/8. Reminder on Standardization.vtt 6KB
  305. 13. Performance Optimization/10. Tensorflow's Eager Memory Usage.vtt 6KB
  306. 2. Algorithm Overview/21. Applying Normalization.vtt 6KB
  307. 10. Natural Binary Classification/10. Encoding Label Values.vtt 6KB
  308. 13. Performance Optimization/18. NaN in Cost History.vtt 6KB
  309. 10. Natural Binary Classification/12. The Sigmoid Equation with Logistic Regression.vtt 6KB
  310. 10. Natural Binary Classification/19. Finishing the Cost Refactor.vtt 6KB
  311. 8. Plotting Data with Javascript/1. Observing Changing Learning Rate and MSE.vtt 6KB
  312. 13. Performance Optimization/21. Improving Model Accuracy.vtt 6KB
  313. 1. What is Machine Learning/6. Identifying Relevant Data.vtt 6KB
  314. 10. Natural Binary Classification/9. Importing Vehicle Data.vtt 6KB
  315. 2. Algorithm Overview/6. Interpreting Bad Results.vtt 6KB
  316. 13. Performance Optimization/17. Plotting Cost History.vtt 6KB
  317. 4. Applications of Tensorflow/2. A Change in Data Structure.vtt 6KB
  318. 14. Appendix Custom CSV Loader/7. Custom Value Parsing.vtt 6KB
  319. 4. Applications of Tensorflow/15. What Now.vtt 6KB
  320. 2. Algorithm Overview/15. Multi-Dimensional KNN.vtt 6KB
  321. 3. Onwards to Tensorflow JS!/8. Logging Tensor Data.vtt 5KB
  322. 13. Performance Optimization/13. Tidying the Training Loop.vtt 5KB
  323. 13. Performance Optimization/8. Measuring Footprint Reduction.vtt 5KB
  324. 4. Applications of Tensorflow/13. Applying Standardization.vtt 5KB
  325. 2. Algorithm Overview/7. Test and Training Data.vtt 5KB
  326. 1. What is Machine Learning/8. Recording Observation Data.vtt 5KB
  327. 5. Getting Started with Gradient Descent/10. Answering Common Questions.vtt 5KB
  328. 11. Multi-Value Classification/3. A Smarter Refactor!.vtt 5KB
  329. 2. Algorithm Overview/8. Randomizing Test Data.vtt 5KB
  330. 2. Algorithm Overview/9. Generalizing KNN.vtt 5KB
  331. 10. Natural Binary Classification/20. Plotting Changing Cost History.vtt 5KB
  332. 7. Increasing Performance with Vectorized Solutions/9. Data Processing in a Helper Method.vtt 5KB
  333. 10. Natural Binary Classification/14. Gauging Classification Accuracy.vtt 5KB
  334. 7. Increasing Performance with Vectorized Solutions/4. Same Results Or Not.vtt 5KB
  335. 14. Appendix Custom CSV Loader/6. Parsing Number Values.vtt 5KB
  336. 13. Performance Optimization/12. Implementing TF Tidy.vtt 5KB
  337. 12. Image Recognition In Action/3. Many Features.vtt 5KB
  338. 4. Applications of Tensorflow/7. Moving to the Editor.vtt 5KB
  339. 11. Multi-Value Classification/13. Calculating Accuracy.vtt 4KB
  340. 6. Gradient Descent with Tensorflow/4. Formulating the Training Loop.vtt 4KB
  341. 6. Gradient Descent with Tensorflow/7. Updating Coefficients.vtt 4KB
  342. 2. Algorithm Overview/11. Printing a Report.vtt 4KB
  343. 13. Performance Optimization/7. Releasing References.vtt 4KB
  344. 1. What is Machine Learning/5. Problem Outline.vtt 4KB
  345. 7. Increasing Performance with Vectorized Solutions/12. Massaging Learning Rates.vtt 4KB
  346. 5. Getting Started with Gradient Descent/1. Linear Regression.vtt 4KB
  347. 13. Performance Optimization/16. Final Memory Report.vtt 4KB
  348. 14. Appendix Custom CSV Loader/3. Reading Files from Disk.vtt 4KB
  349. 13. Performance Optimization/11. Cleaning up Tensors with Tidy.vtt 4KB
  350. 11. Multi-Value Classification/12. Implementing Accuracy Gauges.vtt 4KB
  351. 14. Appendix Custom CSV Loader/4. Splitting into Columns.vtt 4KB
  352. 2. Algorithm Overview/24. Evaluating Different Feature Values.vtt 4KB
  353. 12. Image Recognition In Action/10. Backfilling Variance.vtt 4KB
  354. 10. Natural Binary Classification/1. Introducing Logistic Regression.vtt 3KB
  355. 14. Appendix Custom CSV Loader/5. Dropping Trailing Columns.vtt 3KB
  356. 13. Performance Optimization/15. One More Optimization.vtt 3KB
  357. 11. Multi-Value Classification/1. Multinominal Logistic Regression.vtt 3KB
  358. 12. Image Recognition In Action/1. Handwriting Recognition.vtt 3KB
  359. 1. What is Machine Learning/4. App Setup.vtt 3KB
  360. 14. Appendix Custom CSV Loader/1. Loading CSV Files.vtt 3KB
  361. 12. Image Recognition In Action/7. Unchanging Accuracy.vtt 3KB
  362. 14. Appendix Custom CSV Loader/2. A Test Dataset.vtt 3KB
  363. 13. Performance Optimization/20. Massaging Learning Parameters.vtt 2KB
  364. 13. Performance Optimization/9. Optimization Tensorflow Memory Usage.vtt 2KB
  365. 13. Performance Optimization/14. Measuring Reduced Memory Usage.vtt 2KB
  366. 10. Natural Binary Classification/6. Changes for Logistic Regression.vtt 2KB
  367. 1. What is Machine Learning/1. Getting Started - How to Get Help.vtt 2KB
  368. 10. Natural Binary Classification/8. Project Download.html 215B
  369. 3. Onwards to Tensorflow JS!/4. Tensor Dimension and Shapes.html 139B
  370. 3. Onwards to Tensorflow JS!/7. Broadcasting Elementwise Operations.html 139B
  371. [FreeCourseLab.com].url 126B