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[] Udemy - Introduction to Machine Learning & Deep Learning in Python

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文件列表

  1. 17. Convolutional Neural Networks/8. Convolutional neural networks - illustration.vtt 71MB
  2. 2. Installations/3. Installing Keras and TensorFlow.vtt 65MB
  3. 8. Decision Trees/3. Decision trees introduction - information gain.mp4 47MB
  4. 3. Linear Regression/2. Linear regression theory - optimization.mp4 42MB
  5. 12. Neural Networks/29. Neural network example II - iris dataset.mp4 36MB
  6. 3. Linear Regression/1. Linear regression introduction.mp4 26MB
  7. 12. Neural Networks/12. Optimization - cost function.mp4 26MB
  8. 6. Naive Bayes Classifier/7. Naive Bayes example - clustering news.mp4 23MB
  9. 6. Naive Bayes Classifier/5. Text clustering - basics.mp4 22MB
  10. 19. Course Materials (DOWNLOADS)/1.1 PythonMachineLearning.zip.zip 22MB
  11. 7. Support Vector Machine (SVM)/5. Support vector machine example II - iris dataset.mp4 22MB
  12. 4. Logistic Regression/4. Logistic regression example II- credit scoring.mp4 21MB
  13. 14. Computer Vision - Face Detection/2. Viola-Jones algorithm.mp4 21MB
  14. 7. Support Vector Machine (SVM)/1. Support vector machine introduction I - linear case.mp4 21MB
  15. 12. Neural Networks/17. Gradient calculation I - output layer.mp4 20MB
  16. 18. Recurrent Neural Networks/4. Vanishing and exploding gradients problem.mp4 20MB
  17. 12. Neural Networks/13. Simplified feedforward network.mp4 19MB
  18. 8. Decision Trees/2. Decision trees introduction - entropy.mp4 19MB
  19. 12. Neural Networks/2. Axons and neurons in the human brain.mp4 19MB
  20. 11. Clustering/6. K-means clustering - text clustering.mp4 19MB
  21. 8. Decision Trees/7. The Gini-index approach.mp4 19MB
  22. 12. Neural Networks/11. Feedforward neural networks.mp4 18MB
  23. 16. Deep Neural Networks/9. Deep neural network implementation III.mp4 18MB
  24. 18. Recurrent Neural Networks/9. Stock price prediction example II.mp4 18MB
  25. 4. Logistic Regression/1. Logistic regression introduction.mp4 18MB
  26. 12. Neural Networks/28. Neural network example I - XOR problem.mp4 18MB
  27. 6. Naive Bayes Classifier/1. Naive Bayes classifier introduction I.mp4 17MB
  28. 7. Support Vector Machine (SVM)/2. Support vector machine introduction II - non-linear case.mp4 17MB
  29. 18. Recurrent Neural Networks/5. Long-short term memory (LTSM) model.mp4 17MB
  30. 3. Linear Regression/4. Linear regression implementation I.mp4 17MB
  31. 12. Neural Networks/5. Artificial neurons - the model.mp4 17MB
  32. 17. Convolutional Neural Networks/10. Handwritten digit classification I.mp4 16MB
  33. 7. Support Vector Machine (SVM)/6. Support vector machine example III - digit recognition.mp4 16MB
  34. 12. Neural Networks/3. Modeling human brain.mp4 16MB
  35. 14. Computer Vision - Face Detection/8. Face detection implementation II - CascadeClassifier.mp4 16MB
  36. 16. Deep Neural Networks/8. Deep neural network implementation II.mp4 16MB
  37. 17. Convolutional Neural Networks/11. Handwritten digit classification II.mp4 16MB
  38. 16. Deep Neural Networks/2. Activation functions revisited.mp4 15MB
  39. 18. Recurrent Neural Networks/13. Stock price prediction example VI.mp4 15MB
  40. 16. Deep Neural Networks/7. Deep neural network implementation I.mp4 15MB
  41. 12. Neural Networks/14. Feedforward neural network topology.mp4 15MB
  42. 18. Recurrent Neural Networks/11. Stock price prediction example IV.mp4 15MB
  43. 12. Neural Networks/6. Artificial neurons - activation functions.mp4 14MB
  44. 11. Clustering/2. Principal component analysis example.mp4 14MB
  45. 12. Neural Networks/16. Error calculation.mp4 14MB
  46. 10. Boosting/3. Boosting introduction - equations.mp4 14MB
  47. 11. Clustering/3. K-means clustering introduction I.mp4 14MB
  48. 11. Clustering/9. Hierarchical clustering introduction.mp4 14MB
  49. 8. Decision Trees/5. Decision trees implementation.mp4 14MB
  50. 12. Neural Networks/15. The learning algorithm.mp4 13MB
  51. 4. Logistic Regression/3. Logistic regression example I - sigmoid function.mp4 13MB
  52. 10. Boosting/4. Boosting introduction - final formula.mp4 13MB
  53. 18. Recurrent Neural Networks/3. Recurrent neural networks basics.mp4 13MB
  54. 12. Neural Networks/25. Building networks.mp4 13MB
  55. 12. Neural Networks/19. Backpropagation.mp4 13MB
  56. 14. Computer Vision - Face Detection/3. Haar-features.mp4 13MB
  57. 10. Boosting/5. Boosting implementation I - iris dataset.mp4 12MB
  58. 14. Computer Vision - Face Detection/5. Boosting in computer vision.mp4 12MB
  59. 16. Deep Neural Networks/4. Gradient descent stochastic gradient descent.mp4 12MB
  60. 12. Neural Networks/26. Building networks II.mp4 12MB
  61. 11. Clustering/10. Hierarchical clustering example.mp4 12MB
  62. 8. Decision Trees/1. Decision trees introduction - basics.mp4 12MB
  63. 4. Logistic Regression/6. Cross validation introduction.mp4 12MB
  64. 9. Random Forest Classifier/2. Bagging introduction.mp4 12MB
  65. 12. Neural Networks/7. Artificial neurons - an example.mp4 11MB
  66. 9. Random Forest Classifier/4. Random forests example I - iris dataset.mp4 11MB
  67. 3. Linear Regression/3. Linear regression theory - gradient descent.mp4 11MB
  68. 16. Deep Neural Networks/11. Multiclass classification implementation I.mp4 11MB
  69. 18. Recurrent Neural Networks/8. Stock price prediction example I.mp4 11MB
  70. 11. Clustering/7. DBSCAN introduction.mp4 11MB
  71. 4. Logistic Regression/5. Logistic regression example III - credit scoring.mp4 11MB
  72. 12. Neural Networks/8. Neural networks - the big picture.mp4 11MB
  73. 13. Machine Learning in Finance/3. Predicting stock prices logistic regression.mp4 11MB
  74. 14. Computer Vision - Face Detection/7. Face detection implementation I - installing OpenCV.mp4 11MB
  75. 7. Support Vector Machine (SVM)/4. Support vector machine example I - simple.mp4 10MB
  76. 17. Convolutional Neural Networks/12. Handwritten digit classification III.mp4 10MB
  77. 16. Deep Neural Networks/3. Loss functions.mp4 10MB
  78. 10. Boosting/6. Boosting implementation II -tuning.mp4 10MB
  79. 16. Deep Neural Networks/12. Multiclass classification implementation II.mp4 10MB
  80. 6. Naive Bayes Classifier/6. Text clustering - inverse document frequency (TF-IDF).mp4 10MB
  81. 5. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.mp4 10MB
  82. 7. Support Vector Machine (SVM)/3. Support vector machine introduction III - kernels.mp4 10MB
  83. 17. Convolutional Neural Networks/6. Convolutional neural networks - pooling.mp4 10MB
  84. 9. Random Forest Classifier/1. Pruning introduction.mp4 10MB
  85. 17. Convolutional Neural Networks/2. Convolutional neural networks basics.mp4 10MB
  86. 14. Computer Vision - Face Detection/4. Integral images.mp4 10MB
  87. 12. Neural Networks/22. Applications of neural networks II - stock market forecast.mp4 10MB
  88. 5. K-Nearest Neighbor Classifier/1. K-nearest neighbor introduction.mp4 9MB
  89. 11. Clustering/4. K-means clustering introduction II.mp4 9MB
  90. 12. Neural Networks/23. Deep learning.mp4 9MB
  91. 11. Clustering/5. K-means clustering example.mp4 9MB
  92. 9. Random Forest Classifier/6. Random forests example III - parameter tuning.mp4 9MB
  93. 12. Neural Networks/18. Gradient calculation II - hidden layer.mp4 9MB
  94. 12. Neural Networks/21. Applications of neural networks I - character recognition.mp4 9MB
  95. 3. Linear Regression/5. Linear regression implementation II.mp4 9MB
  96. 14. Computer Vision - Face Detection/10. Face detection implementation IV - tuning the parameters.mp4 9MB
  97. 9. Random Forest Classifier/3. Random forest classifier introduction.mp4 9MB
  98. 13. Machine Learning in Finance/5. Predicting stock prices support vector machine.mp4 9MB
  99. 5. K-Nearest Neighbor Classifier/3. K-nearest neighbor introduction - Euclidean-distance.mp4 9MB
  100. 14. Computer Vision - Face Detection/9. Face detection implementation III - CascadeClassifier parameters.mp4 9MB
  101. 11. Clustering/1. Principal component anlysis introduction.mp4 9MB
  102. 6. Naive Bayes Classifier/2. Naive Bayes classifier introduction II - illustration.mp4 8MB
  103. 17. Convolutional Neural Networks/7. Convolutional neural networks - flattening.mp4 8MB
  104. 10. Boosting/1. Boosting introduction - basics.mp4 8MB
  105. 16. Deep Neural Networks/5. Hyperparameters.mp4 8MB
  106. 10. Boosting/2. Boosting introduction - illustration.mp4 8MB
  107. 5. K-Nearest Neighbor Classifier/2. K-nearest neighbor introduction - lazy learning.mp4 8MB
  108. 1. Introduction/2. Introduction to machine learning.mp4 8MB
  109. 6. Naive Bayes Classifier/3. Naive Bayes classifier implementation.mp4 8MB
  110. 13. Machine Learning in Finance/2. Fetching data from Yahoo Finance.mp4 8MB
  111. 5. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.mp4 8MB
  112. 11. Clustering/8. DBSCAN example.mp4 8MB
  113. 17. Convolutional Neural Networks/5. Convolutional neural networks - kernel II.mp4 8MB
  114. 16. Deep Neural Networks/1. Deep neural networks.mp4 8MB
  115. 18. Recurrent Neural Networks/2. Why do recurrent neural networks are important.mp4 8MB
  116. 18. Recurrent Neural Networks/14. Stock price prediction example VII.mp4 7MB
  117. 13. Machine Learning in Finance/4. Predicting stock prices k-nearest neighbor.mp4 7MB
  118. 5. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.mp4 7MB
  119. 17. Convolutional Neural Networks/3. Feature selection.mp4 7MB
  120. 18. Recurrent Neural Networks/12. Stock price prediction example V.mp4 7MB
  121. 4. Logistic Regression/2. Logistic regression introduction II.mp4 7MB
  122. 8. Decision Trees/6. Decision trees implementation II.mp4 7MB
  123. 8. Decision Trees/6. Decision trees implementation II.vtt 7MB
  124. 12. Neural Networks/4. Learning paradigms.mp4 7MB
  125. 17. Convolutional Neural Networks/4. Convolutional neural networks - kernel.mp4 6MB
  126. 14. Computer Vision - Face Detection/6. Cascading.mp4 6MB
  127. 12. Neural Networks/27. Handling datasets.mp4 6MB
  128. 17. Convolutional Neural Networks/8. Convolutional neural networks - illustration.mp4 6MB
  129. 2. Installations/3. Installing Keras and TensorFlow.mp4 6MB
  130. 14. Computer Vision - Face Detection/1. Computer vision introduction.mp4 6MB
  131. 13. Machine Learning in Finance/1. Stock market basics.mp4 6MB
  132. 15. Deep Learning/1. Types of neural networks.mp4 5MB
  133. 12. Neural Networks/9. Applications of neural networks.mp4 5MB
  134. 10. Boosting/7. Boosting vs. bagging.mp4 5MB
  135. 18. Recurrent Neural Networks/6. Gated recurrent units (GRUs).mp4 5MB
  136. 18. Recurrent Neural Networks/10. Stock price prediction example III.mp4 5MB
  137. 12. Neural Networks/20. Backpropagation II.mp4 5MB
  138. 2. Installations/1. Installing Anaconda.mp4 4MB
  139. 9. Random Forest Classifier/5. Random forests example II - credit scoring.mp4 4MB
  140. 8. Decision Trees/4. Decision trees introduction - pros and cons.mp4 4MB
  141. 4. Logistic Regression/7. Cross validation example.mp4 4MB
  142. 13. Machine Learning in Finance/6. Predicting stock prices - conclusion.mp4 4MB
  143. 1. Introduction/1. Introduction.mp4 3MB
  144. 2. Installations/2. Installing Spyder.mp4 3MB
  145. 4. Logistic Regression/1. Logistic regression introduction.vtt 14KB
  146. 14. Computer Vision - Face Detection/2. Viola-Jones algorithm.vtt 13KB
  147. 18. Recurrent Neural Networks/5. Long-short term memory (LTSM) model.vtt 12KB
  148. 12. Neural Networks/12. Optimization - cost function.vtt 12KB
  149. 16. Deep Neural Networks/2. Activation functions revisited.vtt 11KB
  150. 18. Recurrent Neural Networks/4. Vanishing and exploding gradients problem.vtt 11KB
  151. 6. Naive Bayes Classifier/7. Naive Bayes example - clustering news.vtt 10KB
  152. 8. Decision Trees/7. The Gini-index approach.vtt 10KB
  153. 18. Recurrent Neural Networks/3. Recurrent neural networks basics.vtt 10KB
  154. 7. Support Vector Machine (SVM)/1. Support vector machine introduction I - linear case.vtt 10KB
  155. 8. Decision Trees/2. Decision trees introduction - entropy.vtt 10KB
  156. 6. Naive Bayes Classifier/5. Text clustering - basics.vtt 10KB
  157. 6. Naive Bayes Classifier/1. Naive Bayes classifier introduction I.vtt 9KB
  158. 3. Linear Regression/1. Linear regression introduction.vtt 9KB
  159. 12. Neural Networks/2. Axons and neurons in the human brain.vtt 9KB
  160. 12. Neural Networks/17. Gradient calculation I - output layer.vtt 9KB
  161. 17. Convolutional Neural Networks/11. Handwritten digit classification II.vtt 9KB
  162. 9. Random Forest Classifier/2. Bagging introduction.vtt 9KB
  163. 12. Neural Networks/13. Simplified feedforward network.vtt 9KB
  164. 10. Boosting/4. Boosting introduction - final formula.vtt 9KB
  165. 14. Computer Vision - Face Detection/3. Haar-features.vtt 9KB
  166. 12. Neural Networks/11. Feedforward neural networks.vtt 9KB
  167. 8. Decision Trees/1. Decision trees introduction - basics.vtt 9KB
  168. 8. Decision Trees/3. Decision trees introduction - information gain.vtt 9KB
  169. 7. Support Vector Machine (SVM)/5. Support vector machine example II - iris dataset.vtt 8KB
  170. 8. Decision Trees/5. Decision trees implementation.vtt 8KB
  171. 12. Neural Networks/3. Modeling human brain.vtt 8KB
  172. 16. Deep Neural Networks/4. Gradient descent stochastic gradient descent.vtt 8KB
  173. 3. Linear Regression/2. Linear regression theory - optimization.vtt 8KB
  174. 4. Logistic Regression/4. Logistic regression example II- credit scoring.vtt 8KB
  175. 12. Neural Networks/29. Neural network example II - iris dataset.vtt 8KB
  176. 7. Support Vector Machine (SVM)/2. Support vector machine introduction II - non-linear case.vtt 8KB
  177. 4. Logistic Regression/3. Logistic regression example I - sigmoid function.vtt 8KB
  178. 3. Linear Regression/3. Linear regression theory - gradient descent.vtt 8KB
  179. 12. Neural Networks/28. Neural network example I - XOR problem.vtt 8KB
  180. 10. Boosting/3. Boosting introduction - equations.vtt 8KB
  181. 11. Clustering/6. K-means clustering - text clustering.vtt 8KB
  182. 14. Computer Vision - Face Detection/8. Face detection implementation II - CascadeClassifier.vtt 7KB
  183. 3. Linear Regression/4. Linear regression implementation I.vtt 7KB
  184. 7. Support Vector Machine (SVM)/6. Support vector machine example III - digit recognition.vtt 7KB
  185. 12. Neural Networks/5. Artificial neurons - the model.vtt 7KB
  186. 9. Random Forest Classifier/1. Pruning introduction.vtt 7KB
  187. 16. Deep Neural Networks/8. Deep neural network implementation II.vtt 7KB
  188. 16. Deep Neural Networks/7. Deep neural network implementation I.vtt 7KB
  189. 11. Clustering/9. Hierarchical clustering introduction.vtt 7KB
  190. 14. Computer Vision - Face Detection/5. Boosting in computer vision.vtt 7KB
  191. 17. Convolutional Neural Networks/2. Convolutional neural networks basics.vtt 7KB
  192. 17. Convolutional Neural Networks/10. Handwritten digit classification I.vtt 7KB
  193. 11. Clustering/3. K-means clustering introduction I.vtt 7KB
  194. 14. Computer Vision - Face Detection/4. Integral images.vtt 7KB
  195. 16. Deep Neural Networks/9. Deep neural network implementation III.vtt 7KB
  196. 17. Convolutional Neural Networks/6. Convolutional neural networks - pooling.vtt 7KB
  197. 16. Deep Neural Networks/3. Loss functions.vtt 7KB
  198. 5. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.vtt 7KB
  199. 18. Recurrent Neural Networks/8. Stock price prediction example I.vtt 7KB
  200. 12. Neural Networks/14. Feedforward neural network topology.vtt 7KB
  201. 12. Neural Networks/6. Artificial neurons - activation functions.vtt 7KB
  202. 18. Recurrent Neural Networks/11. Stock price prediction example IV.vtt 7KB
  203. 12. Neural Networks/25. Building networks.vtt 7KB
  204. 12. Neural Networks/16. Error calculation.vtt 7KB
  205. 11. Clustering/2. Principal component analysis example.vtt 6KB
  206. 5. K-Nearest Neighbor Classifier/1. K-nearest neighbor introduction.vtt 6KB
  207. 4. Logistic Regression/5. Logistic regression example III - credit scoring.vtt 6KB
  208. 17. Convolutional Neural Networks/5. Convolutional neural networks - kernel II.vtt 6KB
  209. 9. Random Forest Classifier/3. Random forest classifier introduction.vtt 6KB
  210. 16. Deep Neural Networks/1. Deep neural networks.vtt 6KB
  211. 1. Introduction/2. Introduction to machine learning.vtt 6KB
  212. 5. K-Nearest Neighbor Classifier/3. K-nearest neighbor introduction - Euclidean-distance.vtt 6KB
  213. 10. Boosting/5. Boosting implementation I - iris dataset.vtt 6KB
  214. 10. Boosting/2. Boosting introduction - illustration.vtt 6KB
  215. 16. Deep Neural Networks/5. Hyperparameters.vtt 6KB
  216. 11. Clustering/10. Hierarchical clustering example.vtt 6KB
  217. 16. Deep Neural Networks/11. Multiclass classification implementation I.vtt 6KB
  218. 12. Neural Networks/15. The learning algorithm.vtt 6KB
  219. 4. Logistic Regression/6. Cross validation introduction.vtt 6KB
  220. 12. Neural Networks/26. Building networks II.vtt 6KB
  221. 12. Neural Networks/19. Backpropagation.vtt 6KB
  222. 17. Convolutional Neural Networks/7. Convolutional neural networks - flattening.vtt 6KB
  223. 16. Deep Neural Networks/12. Multiclass classification implementation II.vtt 6KB
  224. 17. Convolutional Neural Networks/12. Handwritten digit classification III.vtt 6KB
  225. 11. Clustering/5. K-means clustering example.vtt 5KB
  226. 18. Recurrent Neural Networks/13. Stock price prediction example VI.vtt 5KB
  227. 11. Clustering/7. DBSCAN introduction.vtt 5KB
  228. 3. Linear Regression/5. Linear regression implementation II.vtt 5KB
  229. 9. Random Forest Classifier/4. Random forests example I - iris dataset.vtt 5KB
  230. 10. Boosting/6. Boosting implementation II -tuning.vtt 5KB
  231. 6. Naive Bayes Classifier/6. Text clustering - inverse document frequency (TF-IDF).vtt 5KB
  232. 9. Random Forest Classifier/6. Random forests example III - parameter tuning.vtt 5KB
  233. 18. Recurrent Neural Networks/2. Why do recurrent neural networks are important.vtt 5KB
  234. 20. DISCOUNT FOR OTHER COURSES!/1. 90% OFF For Other Courses.html 5KB
  235. 6. Naive Bayes Classifier/3. Naive Bayes classifier implementation.vtt 5KB
  236. 11. Clustering/8. DBSCAN example.vtt 5KB
  237. 7. Support Vector Machine (SVM)/3. Support vector machine introduction III - kernels.vtt 5KB
  238. 10. Boosting/1. Boosting introduction - basics.vtt 5KB
  239. 6. Naive Bayes Classifier/2. Naive Bayes classifier introduction II - illustration.vtt 5KB
  240. 12. Neural Networks/8. Neural networks - the big picture.vtt 5KB
  241. 17. Convolutional Neural Networks/3. Feature selection.vtt 5KB
  242. 17. Convolutional Neural Networks/4. Convolutional neural networks - kernel.vtt 5KB
  243. 14. Computer Vision - Face Detection/6. Cascading.vtt 5KB
  244. 12. Neural Networks/7. Artificial neurons - an example.vtt 5KB
  245. 14. Computer Vision - Face Detection/7. Face detection implementation I - installing OpenCV.vtt 5KB
  246. 12. Neural Networks/22. Applications of neural networks II - stock market forecast.vtt 5KB
  247. 5. K-Nearest Neighbor Classifier/2. K-nearest neighbor introduction - lazy learning.vtt 5KB
  248. 18. Recurrent Neural Networks/9. Stock price prediction example II.vtt 5KB
  249. 12. Neural Networks/23. Deep learning.vtt 5KB
  250. 5. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.vtt 5KB
  251. 11. Clustering/4. K-means clustering introduction II.vtt 5KB
  252. 7. Support Vector Machine (SVM)/4. Support vector machine example I - simple.vtt 5KB
  253. 14. Computer Vision - Face Detection/9. Face detection implementation III - CascadeClassifier parameters.vtt 4KB
  254. 12. Neural Networks/21. Applications of neural networks I - character recognition.vtt 4KB
  255. 14. Computer Vision - Face Detection/1. Computer vision introduction.vtt 4KB
  256. 4. Logistic Regression/2. Logistic regression introduction II.vtt 4KB
  257. 15. Deep Learning/1. Types of neural networks.vtt 4KB
  258. 13. Machine Learning in Finance/3. Predicting stock prices logistic regression.vtt 4KB
  259. 13. Machine Learning in Finance/2. Fetching data from Yahoo Finance.vtt 4KB
  260. 11. Clustering/1. Principal component anlysis introduction.vtt 4KB
  261. 12. Neural Networks/18. Gradient calculation II - hidden layer.vtt 4KB
  262. 18. Recurrent Neural Networks/6. Gated recurrent units (GRUs).vtt 4KB
  263. 18. Recurrent Neural Networks/12. Stock price prediction example V.vtt 4KB
  264. 13. Machine Learning in Finance/5. Predicting stock prices support vector machine.vtt 4KB
  265. 13. Machine Learning in Finance/1. Stock market basics.vtt 4KB
  266. 10. Boosting/7. Boosting vs. bagging.vtt 4KB
  267. 5. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.vtt 3KB
  268. 13. Machine Learning in Finance/4. Predicting stock prices k-nearest neighbor.vtt 3KB
  269. 14. Computer Vision - Face Detection/10. Face detection implementation IV - tuning the parameters.vtt 3KB
  270. 18. Recurrent Neural Networks/14. Stock price prediction example VII.vtt 3KB
  271. 12. Neural Networks/27. Handling datasets.vtt 3KB
  272. 12. Neural Networks/4. Learning paradigms.vtt 3KB
  273. 8. Decision Trees/4. Decision trees introduction - pros and cons.vtt 3KB
  274. 18. Recurrent Neural Networks/10. Stock price prediction example III.vtt 3KB
  275. 4. Logistic Regression/7. Cross validation example.vtt 3KB
  276. 1. Introduction/1. Introduction.vtt 2KB
  277. 12. Neural Networks/9. Applications of neural networks.vtt 2KB
  278. 2. Installations/1. Installing Anaconda.vtt 2KB
  279. 12. Neural Networks/20. Backpropagation II.vtt 2KB
  280. 9. Random Forest Classifier/5. Random forests example II - credit scoring.vtt 2KB
  281. 13. Machine Learning in Finance/6. Predicting stock prices - conclusion.vtt 2KB
  282. 2. Installations/2. Installing Spyder.vtt 2KB
  283. 5. K-Nearest Neighbor Classifier/4. UPDATE bias and variance.html 333B
  284. 16. Deep Neural Networks/13. ARTICLE Optimizers Explained (SGD, ADAGrad, ADAM...).html 248B
  285. 17. Convolutional Neural Networks/13. ARTICLE Regularization (L1, L2 and dropout).html 232B
  286. 6. Naive Bayes Classifier/4. ----- TEXT CLASSIFICATION -----.html 193B
  287. 19. Course Materials (DOWNLOADS)/2.1 house_prices.csv.csv 183B
  288. 17. Convolutional Neural Networks/9. ----- HANDWRITTEN DIGITS -----.html 164B
  289. 18. Recurrent Neural Networks/1. ----- RNN THEORY -----.html 146B
  290. 16. Deep Neural Networks/10. ----- IRIS DATASET -----.html 141B
  291. 0. Websites you may like/[FCS Forum].url 133B
  292. 17. Convolutional Neural Networks/1. ----- CNN THEORY -----.html 130B
  293. 0. Websites you may like/[FreeCourseSite.com].url 127B
  294. 18. Recurrent Neural Networks/7. --- STOCK MAKRET ---.html 124B
  295. 0. Websites you may like/[CourseClub.ME].url 122B
  296. 16. Deep Neural Networks/6. ----- XOR PROBLEM -----.html 117B
  297. 19. Course Materials (DOWNLOADS)/1. Course materials.html 70B
  298. 19. Course Materials (DOWNLOADS)/2. House prices csv file.html 55B
  299. 12. Neural Networks/24. ----- IMPLEMENTATION -----.html 53B
  300. 12. Neural Networks/10. ---- BACKPROPAGATION ----.html 42B
  301. 12. Neural Networks/1. ---- NEURAL NETWORKS INTRODUCTION ----.html 35B