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投影片 1 (Lab531, 2:12:56)
 
 
 
  • 1. Slide 1
  • 2. Latent Semantic Analysis (LSA)
  • 3. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation
  • 4. Latent Semantic Analysis (LSA)
  • 5. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation
  • 6. Latent Semantic Analysis (LSA)
  • 7. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation
  • 8. Latent Semantic Analysis (LSA)
  • 9. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation
  • 10. Latent Semantic Analysis (LSA)
  • 11. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation
  • 12. Latent Semantic Analysis (LSA)
  • 13. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation
  • 14. Latent Semantic Analysis (LSA)
  • 15. Dimensionality Reduction (1/2)
  • 16. Latent Semantic Analysis (LSA)
  • 17. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation
  • 18. Latent Semantic Analysis (LSA)
  • 19. Dimensionality Reduction (1/2)
  • 20. Latent Semantic Analysis (LSA)
  • 21. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation
  • 22. Latent Semantic Analysis (LSA)
  • 23. Dimensionality Reduction (1/2)
  • 24. Latent Semantic Analysis (LSA)
  • 25. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation
  • 26. Latent Semantic Analysis (LSA)
  • 27. Dimensionality Reduction (1/2)
  • 28. Latent Semantic Analysis (LSA)
  • 29. Dimensionality Reduction (1/2)
  • 30. Dimensionality Reduction (2/2)
  • 31. Dimensionality Reduction (1/2)
  • 32. Dimensionality Reduction (2/2)
  • 33. Dimensionality Reduction (1/2)
  • 34. Dimensionality Reduction (2/2)
  • 35. Dimensionality Reduction (1/2)
  • 36. Dimensionality Reduction (2/2)
  • 37. Dimensionality Reduction (1/2)
  • 38. Dimensionality Reduction (2/2)
  • 39. Dimensionality Reduction (1/2)
  • 40. Dimensionality Reduction (2/2)
  • 41. Singular Value Decomposition (SVD)
  • 42. Dimensionality Reduction (2/2)
  • 43. Dimensionality Reduction (1/2)
  • 44. Dimensionality Reduction (2/2)
  • 45. Singular Value Decomposition (SVD)
  • 46. Dimensionality Reduction (2/2)
  • 47. Dimensionality Reduction (1/2)
  • 48. Dimensionality Reduction (2/2)
  • 49. Singular Value Decomposition (SVD)
  • 50. Singular Value Decomposition (SVD)
  • 51. Singular Value Decomposition (SVD)
  • 52. Dimensionality Reduction (2/2)
  • 53. Dimensionality Reduction (1/2)
  • 54. Dimensionality Reduction (2/2)
  • 55. Singular Value Decomposition (SVD)
  • 56. Dimensionality Reduction (2/2)
  • 57. Dimensionality Reduction (1/2)
  • 58. Dimensionality Reduction (2/2)
  • 59. Singular Value Decomposition (SVD)
  • 60. Dimensionality Reduction (2/2)
  • 61. Dimensionality Reduction (1/2)
  • 62. Dimensionality Reduction (2/2)
  • 63. Singular Value Decomposition (SVD)
  • 64. Dimensionality Reduction (2/2)
  • 65. Singular Value Decomposition (SVD)
  • 66. Dimensionality Reduction (2/2)
  • 67. Dimensionality Reduction (1/2)
  • 68. Dimensionality Reduction (2/2)
  • 69. Singular Value Decomposition (SVD)
  • 70. Dimensionality Reduction (2/2)
  • 71. Dimensionality Reduction (1/2)
  • 72. Dimensionality Reduction (2/2)
  • 73. Singular Value Decomposition (SVD)
  • 74. Singular Value Decomposition (SVD)
  • 75. Singular Value Decomposition (SVD)
  • 76. Dimensionality Reduction (2/2)
  • 77. Dimensionality Reduction (1/2)
  • 78. Latent Semantic Analysis (LSA)
  • 79. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation
  • 80. Latent Semantic Analysis (LSA)
  • 81. Dimensionality Reduction (1/2)
  • 82. Dimensionality Reduction (2/2)
  • 83. Singular Value Decomposition (SVD)
  • 84. Singular Value Decomposition (SVD)
  • 85. Singular Value Decomposition (SVD)
  • 86. Singular Value Decomposition (SVD)
  • 87. Singular Value Decomposition (SVD)
  • 88. Singular Value Decomposition (SVD)
  • 89. Singular Value Decomposition (SVD)
  • 90. Singular Value Decomposition (SVD)
  • 91. Dimensionality Reduction (2/2)
  • 92. Dimensionality Reduction (1/2)
  • 93. Dimensionality Reduction (2/2)
  • 94. Dimensionality Reduction (1/2)
  • 95. Dimensionality Reduction (2/2)
  • 96. Singular Value Decomposition (SVD)
  • 97. Singular Value Decomposition (SVD)
  • 98. Singular Value Decomposition (SVD)
  • 99. Singular Value Decomposition (SVD)
  • 100. Singular Value Decomposition (SVD)
  • 101. Singular Value Decomposition (SVD)
  • 102. Singular Value Decomposition (SVD)
  • 103. Singular Value Decomposition (SVD)
  • 104. Singular Value Decomposition (SVD)
  • 105. Singular Value Decomposition (SVD)
  • 106. Singular Value Decomposition (SVD)
  • 107. Singular Value Decomposition (SVD)
  • 108. Singular Value Decomposition (SVD)
  • 109. Singular Value Decomposition (SVD)
  • 110. Singular Value Decomposition (SVD)
  • 111. Singular Value Decomposition (SVD)
  • 112. Singular Value Decomposition (SVD)
  • 113. Example Applications in Linguistic Processing
  • 114. Singular Value Decomposition (SVD)
  • 115. Singular Value Decomposition (SVD)
  • 116. Singular Value Decomposition (SVD)
  • 117. Singular Value Decomposition (SVD)
  • 118. Singular Value Decomposition (SVD)
  • 119. Singular Value Decomposition (SVD)
  • 120. Singular Value Decomposition (SVD)
  • 121. Example Applications in Linguistic Processing
  • 122. Cosine Similarity
  • 123. Example Applications in Linguistic Processing
  • 124. Singular Value Decomposition (SVD)
  • 125. Singular Value Decomposition (SVD)
  • 126. Singular Value Decomposition (SVD)
  • 127. Example Applications in Linguistic Processing
  • 128. Singular Value Decomposition (SVD)
  • 129. Singular Value Decomposition (SVD)
  • 130. Singular Value Decomposition (SVD)
  • 131. Example Applications in Linguistic Processing
  • 132. Cosine Similarity
  • 133. Example Applications in Linguistic Processing
  • 134. Cosine Similarity
  • 135. Example Applications in Linguistic Processing
  • 136. Cosine Similarity
  • 137. Example Applications in Linguistic Processing
  • 138. Cosine Similarity
  • 139. Example Applications in Linguistic Processing
  • 140. Singular Value Decomposition (SVD)
  • 141. Singular Value Decomposition (SVD)
  • 142. Singular Value Decomposition (SVD)
  • 143. Example Applications in Linguistic Processing
  • 144. Cosine Similarity
  • 145. Example Applications in Linguistic Processing
  • 146. Cosine Similarity
  • 147. Example Applications in Linguistic Processing
  • 148. Singular Value Decomposition (SVD)
  • 149. Singular Value Decomposition (SVD)
  • 150. Singular Value Decomposition (SVD)
  • 151. Example Applications in Linguistic Processing
  • 152. Cosine Similarity
  • 153. Example Applications in Linguistic Processing
  • 154. Cosine Similarity
  • 155. Example Applications in Linguistic Processing
  • 156. Singular Value Decomposition (SVD)
  • 157. Singular Value Decomposition (SVD)
  • 158. Singular Value Decomposition (SVD)
  • 159. Singular Value Decomposition (SVD)
  • 160. Singular Value Decomposition (SVD)
  • 161. Example Applications in Linguistic Processing
  • 162. Cosine Similarity
  • 163. Example Applications in Linguistic Processing
  • 164. Integration with N-gram Language Models
  • 165. Example Applications in Linguistic Processing
  • 166. Integration with N-gram Language Models
  • 167. Probabilistic Latent Semantic Analysis (PLSA)
  • 168. Integration with N-gram Language Models
  • 169. Example Applications in Linguistic Processing
  • 170. Cosine Similarity
  • 171. Example Applications in Linguistic Processing
  • 172. Integration with N-gram Language Models
  • 173. Example Applications in Linguistic Processing
  • 174. Cosine Similarity
  • 175. Example Applications in Linguistic Processing
  • 176. Singular Value Decomposition (SVD)
  • 177. Singular Value Decomposition (SVD)
  • 178. Singular Value Decomposition (SVD)
  • 179. Example Applications in Linguistic Processing
  • 180. Cosine Similarity
  • 181. Example Applications in Linguistic Processing
  • 182. Integration with N-gram Language Models
  • 183. Example Applications in Linguistic Processing
  • 184. Cosine Similarity
  • 185. Example Applications in Linguistic Processing
  • 186. Singular Value Decomposition (SVD)
  • 187. Singular Value Decomposition (SVD)
  • 188. Singular Value Decomposition (SVD)
  • 189. Dimensionality Reduction (2/2)
  • 190. Dimensionality Reduction (1/2)
  • 191. Latent Semantic Analysis (LSA)
  • 192. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation
  • 193. Latent Semantic Analysis (LSA)
  • 194. Dimensionality Reduction (1/2)
  • 195. Dimensionality Reduction (2/2)
  • 196. Singular Value Decomposition (SVD)
  • 197. Singular Value Decomposition (SVD)
  • 198. Singular Value Decomposition (SVD)
  • 199. Example Applications in Linguistic Processing
  • 200. Cosine Similarity
  • 201. Example Applications in Linguistic Processing
  • 202. Integration with N-gram Language Models
  • 203. Probabilistic Latent Semantic Analysis (PLSA)
  • 204. Probabilistic Latent Semantic Analysis (PLSA)
  • 205. Probabilistic Latent Semantic Analysis (PLSA)
  • 206. Probabilistic Latent Semantic Analysis (PLSA)
  • 207. Probabilistic Latent Semantic Analysis (PLSA)
  • 208. Probabilistic Latent Semantic Analysis (PLSA)
  • 209. Probabilistic Latent Semantic Analysis (PLSA)
  • 210. Probabilistic Latent Semantic Analysis (PLSA)
  • 211. Latent Dirichlet Allocation(LDA)
  • 212. Probabilistic Latent Semantic Analysis (PLSA)
  • 213. Latent Dirichlet Allocation(LDA)
  • 214. Probabilistic Latent Semantic Analysis (PLSA)
  • 215. Latent Dirichlet Allocation(LDA)
  • 216. Gibbs Sampling in general
  • 217. Gibbs Sampling applied on LDA
  • 218. Gibbs Sampling applied on LDA
  • 219. Gibbs Sampling applied on LDA
  • 220. Gibbs Sampling applied on LDA
  • 221. Gibbs Sampling applied on LDA
  • 222. Matrix Factorization (MF) for Recommendation systems
  • 223. Gibbs Sampling applied on LDA
  • 224. Gibbs Sampling applied on LDA
  • 225. Gibbs Sampling applied on LDA
  • 226. Gibbs Sampling applied on LDA
  • 227. Gibbs Sampling applied on LDA
  • 228. Gibbs Sampling in general
  • 229. Gibbs Sampling applied on LDA
  • 230. Gibbs Sampling applied on LDA
  • 231. Gibbs Sampling applied on LDA
  • 232. Gibbs Sampling applied on LDA
  • 233. Gibbs Sampling applied on LDA
  • 234. Gibbs Sampling applied on LDA
  • 235. Gibbs Sampling applied on LDA
  • 236. Gibbs Sampling applied on LDA
  • 237. Gibbs Sampling applied on LDA
  • 238. Gibbs Sampling in general
  • 239. Latent Dirichlet Allocation(LDA)
  • 240. Gibbs Sampling in general
  • 241. Gibbs Sampling applied on LDA
  • 242. Gibbs Sampling applied on LDA
  • 243. Gibbs Sampling applied on LDA
  • 244. Gibbs Sampling applied on LDA
  • 245. Gibbs Sampling applied on LDA
  • 246. Matrix Factorization (MF) for Recommendation systems
  • 247. Matrix Factorization (MF) for Recommendation systems
  • 248. Matrix Factorization (MF)
  • 249. Matrix Factorization (MF) for Recommendation systems
  • 250. Matrix Factorization (MF)
  • 251. Matrix Factorization (MF)
  • 252. Matrix Factorization (MF)
  • 253. Matrix Factorization (MF)
  • 254. Matrix Factorization (MF)
  • 255. Matrix Factorization (MF)
  • 256. Matrix Factorization (MF)
  • 257. Matrix Factorization (MF) for Recommendation systems
  • 258. Matrix Factorization (MF)
  • 259. Matrix Factorization (MF)
  • 260. Overfitting Problem
  • 261. Matrix Factorization (MF)
  • 262. Overfitting Problem
  • 263. Extensions of Matrix Factorization (MF)
  • 264. Overfitting Problem
  • 265. Matrix Factorization (MF)
  • 266. Matrix Factorization (MF)
  • 267. Matrix Factorization (MF)
  • 268. Overfitting Problem
  • 269. Extensions of Matrix Factorization (MF)
  • 270. References
  • 271. References
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Volume
1.0x
00:00/2:12:56