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[UdemyCourseDownloader] Building Recommender Systems with Machine Learning and AI

  • 收录时间:2018-12-16 01:38:25
  • 文件大小:4GB
  • 下载次数:61
  • 最近下载:2021-01-11 07:31:38
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文件列表

  1. 08 Introduction to Deep Learning [Optional]/056 [Activity] Handwriting Recognition with Tensorflow part 1.mp4 182MB
  2. 08 Introduction to Deep Learning [Optional]/052 [Activity] Playing with Tensorflow.mp4 145MB
  3. 09 Deep Learning for Recommender Systems/070 [Activity] Recommendations with RBMs part 1.mp4 144MB
  4. 08 Introduction to Deep Learning [Optional]/067 [Activity] Sentiment Analysis of Movie Reviews using RNNs and Keras.mp4 120MB
  5. 10 Scaling it Up/087 DSSTNE in Action.mp4 117MB
  6. 01 Getting Started/002 [Activity] Install Anaconda course materials and create movie recommendations.mp4 104MB
  7. 08 Introduction to Deep Learning [Optional]/061 [Exercise] Predict Political Parties of Politicians with Keras.mp4 100MB
  8. 08 Introduction to Deep Learning [Optional]/055 Introduction to Tensorflow.mp4 92MB
  9. 11 Real-World Challenges of Recommender Systems/097 Filter Bubbles Trust and Outliers.mp4 92MB
  10. 08 Introduction to Deep Learning [Optional]/059 [Activity] Handwriting Recognition with Keras.mp4 89MB
  11. 08 Introduction to Deep Learning [Optional]/051 History of Artificial Neural Networks.mp4 84MB
  12. 08 Introduction to Deep Learning [Optional]/064 [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp4 82MB
  13. 08 Introduction to Deep Learning [Optional]/062 Intro to Convolutional Neural Networks (CNNs).mp4 78MB
  14. 09 Deep Learning for Recommender Systems/071 [Activity] Recommendations with RBMs part 2.mp4 77MB
  15. 09 Deep Learning for Recommender Systems/076 [Activity] Recommendations with Deep Neural Networks.mp4 75MB
  16. 10 Scaling it Up/090 SageMaker in Action Factorization Machines on one million ratings in the cloud.mp4 68MB
  17. 03 Evaluating Recommender Systems/018 [Activity] Walkthrough of RecommenderMetrics.py.mp4 64MB
  18. 09 Deep Learning for Recommender Systems/079 Exercise Results GRU4Rec in Action.mp4 63MB
  19. 05 Content-Based Filtering/025 Content-Based Recommendations and the Cosine Similarity Metric.mp4 62MB
  20. 07 Matrix Factorization Methods/043 Principal Component Analysis (PCA).mp4 61MB
  21. 03 Evaluating Recommender Systems/016 Churn Responsiveness and AB Tests.mp4 61MB
  22. 06 Neighborhood-Based Collaborative Filtering/030 Measuring Similarity and Sparsity.mp4 59MB
  23. 11 Real-World Challenges of Recommender Systems/100 Fraud The Perils of Clickstream and International Concerns.mp4 58MB
  24. 08 Introduction to Deep Learning [Optional]/057 [Activity] Handwriting Recognition with Tensorflow part 2.mp4 58MB
  25. 09 Deep Learning for Recommender Systems/080 Bleeding Edge Alert Deep Factorization Machines.mp4 57MB
  26. 10 Scaling it Up/084 [Activity] Movie Recommendations with Spark Matrix Factorization and ALS.mp4 56MB
  27. 03 Evaluating Recommender Systems/019 [Activity] Walkthrough of TestMetrics.py.mp4 54MB
  28. 11 Real-World Challenges of Recommender Systems/101 Temporal Effects and Value-Aware Recommendations.mp4 54MB
  29. 10 Scaling it Up/082 [Activity] Introduction and Installation of Apache Spark.mp4 53MB
  30. 05 Content-Based Filtering/027 [Activity] Producing and Evaluating Content-Based Movie Recommendations.mp4 52MB
  31. 06 Neighborhood-Based Collaborative Filtering/034 Item-based Collaborative Filtering.mp4 52MB
  32. 10 Scaling it Up/085 [Activity] Recommendations from 20 million ratings with Spark.mp4 51MB
  33. 08 Introduction to Deep Learning [Optional]/065 Intro to Recurrent Neural Networks (RNNs).mp4 50MB
  34. 09 Deep Learning for Recommender Systems/077 Clickstream Recommendations with RNNs.mp4 49MB
  35. 06 Neighborhood-Based Collaborative Filtering/033 [Activity] User-based Collaborative Filtering Hands-On.mp4 49MB
  36. 05 Content-Based Filtering/028 [Activity] Bleeding Edge Alert Mise en Scene Recommendations.mp4 47MB
  37. 02 Introduction to Python [Optional]/008 [Activity] The Basics of Python.mp4 43MB
  38. 09 Deep Learning for Recommender Systems/068 Intro to Deep Learning for Recommenders.mp4 43MB
  39. 10 Scaling it Up/086 Amazon DSSTNE.mp4 42MB
  40. 06 Neighborhood-Based Collaborative Filtering/041 [Exercise] Experiment with different KNN parameters..mp4 41MB
  41. 03 Evaluating Recommender Systems/013 Accuracy Metrics (RMSE MAE).mp4 40MB
  42. 04 A Recommender Engine Framework/023 [Activity] Recommender Engine Walkthrough Part 2.mp4 40MB
  43. 14 Wrapping Up/108 More to Explore.mp4 39MB
  44. 11 Real-World Challenges of Recommender Systems/099 Exercise Solution Outlier Removal.mp4 38MB
  45. 08 Introduction to Deep Learning [Optional]/053 Training Neural Networks.mp4 38MB
  46. 04 A Recommender Engine Framework/022 [Activity] Recommender Engine Walkthrough Part 1.mp4 38MB
  47. 09 Deep Learning for Recommender Systems/072 [Activity] Evaluating the RBM Recommender.mp4 38MB
  48. 07 Matrix Factorization Methods/045 [Activity] Running SVD and SVD on MovieLens.mp4 37MB
  49. 01 Getting Started/006 Top-N Recommender Architecture.mp4 37MB
  50. 08 Introduction to Deep Learning [Optional]/050 Deep Learning Pre-Requisites.mp4 37MB
  51. 04 A Recommender Engine Framework/024 [Activity] Review the Results of our Algorithm Evaluation..mp4 35MB
  52. 06 Neighborhood-Based Collaborative Filtering/032 User-based Collaborative Filtering.mp4 34MB
  53. 09 Deep Learning for Recommender Systems/073 [Exercise] Tuning Restricted Boltzmann Machines.mp4 34MB
  54. 13 Hybrid Approaches/107 Exercise Solution Hybrid Recommenders.mp4 33MB
  55. 04 A Recommender Engine Framework/021 Our Recommender Engine Architecture.mp4 33MB
  56. 09 Deep Learning for Recommender Systems/069 Restricted Boltzmann Machines (RBMs).mp4 32MB
  57. 08 Introduction to Deep Learning [Optional]/054 Tuning Neural Networks.mp4 31MB
  58. 06 Neighborhood-Based Collaborative Filtering/031 Similarity Metrics.mp4 31MB
  59. 03 Evaluating Recommender Systems/012 TrainTest and Cross Validation.mp4 29MB
  60. 11 Real-World Challenges of Recommender Systems/091 The Cold Start Problem (and solutions).mp4 28MB
  61. 09 Deep Learning for Recommender Systems/081 More Emerging Tech to Watch.mp4 28MB
  62. 01 Getting Started/003 Course Roadmap.mp4 28MB
  63. 12 Case Studies/104 Case Study Netflix Part 1.mp4 28MB
  64. 12 Case Studies/102 Case Study YouTube Part 1.mp4 27MB
  65. 09 Deep Learning for Recommender Systems/075 Auto-Encoders for Recommendations Deep Learning for Recs.mp4 27MB
  66. 01 Getting Started/004 Types of Recommenders.mp4 27MB
  67. 06 Neighborhood-Based Collaborative Filtering/035 [Activity] Item-based Collaborative Filtering Hands-On.mp4 27MB
  68. 11 Real-World Challenges of Recommender Systems/096 Exercise Solution Implement a Stoplist.mp4 27MB
  69. 12 Case Studies/105 Case Study Netflix Part 2.mp4 27MB
  70. 07 Matrix Factorization Methods/048 Bleeding Edge Alert Sparse Linear Methods (SLIM).mp4 26MB
  71. 12 Case Studies/103 Case Study YouTube Part 2.mp4 26MB
  72. 07 Matrix Factorization Methods/044 Singular Value Decomposition.mp4 25MB
  73. 06 Neighborhood-Based Collaborative Filtering/039 KNN Recommenders.mp4 25MB
  74. 08 Introduction to Deep Learning [Optional]/060 Classifier Patterns with Keras.mp4 25MB
  75. 03 Evaluating Recommender Systems/014 Top-N Hit Rate - Many Ways.mp4 25MB
  76. 02 Introduction to Python [Optional]/009 Data Structures in Python.mp4 24MB
  77. 11 Real-World Challenges of Recommender Systems/093 Exercise Solution Random Exploration.mp4 24MB
  78. 05 Content-Based Filtering/029 [Exercise] Dive Deeper into Content-Based Recommendations.mp4 24MB
  79. 06 Neighborhood-Based Collaborative Filtering/040 [Activity] Running User and Item-Based KNN on MovieLens.mp4 24MB
  80. 07 Matrix Factorization Methods/046 Improving on SVD.mp4 23MB
  81. 08 Introduction to Deep Learning [Optional]/063 CNN Architectures.mp4 23MB
  82. 03 Evaluating Recommender Systems/020 [Activity] Measure the Performance of SVD Recommendations.mp4 22MB
  83. 06 Neighborhood-Based Collaborative Filtering/042 Bleeding Edge Alert Translation-Based Recommendations.mp4 21MB
  84. 01 Getting Started/007 [Quiz] Review the basics of recommender systems..mp4 21MB
  85. 14 Wrapping Up/109 Bonus Lecture Companion Book and More Courses from Sundog Education.mp4 21MB
  86. 08 Introduction to Deep Learning [Optional]/066 Training Recurrent Neural Networks.mp4 21MB
  87. 01 Getting Started/005 Understanding You through Implicit and Explicit Ratings.mp4 21MB
  88. 11 Real-World Challenges of Recommender Systems/094 Stoplists.mp4 20MB
  89. 01 Getting Started/001 Udemy 101 Getting the Most From This Course.mp4 20MB
  90. 06 Neighborhood-Based Collaborative Filtering/036 [Exercise] Tuning Collaborative Filtering Algorithms.mp4 20MB
  91. 05 Content-Based Filtering/026 K-Nearest-Neighbors and Content Recs.mp4 20MB
  92. 13 Hybrid Approaches/106 Hybrid Recommenders and Exercise.mp4 18MB
  93. 08 Introduction to Deep Learning [Optional]/049 Deep Learning Introduction.mp4 18MB
  94. 10 Scaling it Up/083 Apache Spark Architecture.mp4 17MB
  95. 08 Introduction to Deep Learning [Optional]/058 Introduction to Keras.mp4 16MB
  96. 10 Scaling it Up/089 AWS SageMaker and Factorization Machines.mp4 16MB
  97. 06 Neighborhood-Based Collaborative Filtering/037 [Activity] Evaluating Collaborative Filtering Systems Offline.mp4 15MB
  98. 02 Introduction to Python [Optional]/011 [Exercise] Booleans loops and a hands-on challenge.mp4 14MB
  99. 03 Evaluating Recommender Systems/015 Coverage Diversity and Novelty.mp4 14MB
  100. 03 Evaluating Recommender Systems/017 [Quiz] Review ways to measure your recommender..mp4 13MB
  101. 07 Matrix Factorization Methods/047 [Exercise] Tune the hyperparameters on SVD.mp4 12MB
  102. 02 Introduction to Python [Optional]/010 Functions in Python.mp4 12MB
  103. 09 Deep Learning for Recommender Systems/074 Exercise Results Tuning a RBM Recommender.mp4 12MB
  104. 10 Scaling it Up/088 Scaling Up DSSTNE.mp4 10MB
  105. 06 Neighborhood-Based Collaborative Filtering/038 [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering.mp4 9MB
  106. 09 Deep Learning for Recommender Systems/078 [Exercise] Get GRU4Rec Working on your Desktop.mp4 7MB
  107. 11 Real-World Challenges of Recommender Systems/092 [Exercise] Implement Random Exploration.mp4 2MB
  108. 11 Real-World Challenges of Recommender Systems/098 [Exercise] Identify and Eliminate Outlier Users.mp4 2MB
  109. 11 Real-World Challenges of Recommender Systems/095 [Exercise] Implement a Stoplist.mp4 1MB
  110. 08 Introduction to Deep Learning [Optional]/056 [Activity] Handwriting Recognition with Tensorflow part 1-en.srt 33KB
  111. 08 Introduction to Deep Learning [Optional]/055 Introduction to Tensorflow-en.srt 26KB
  112. 09 Deep Learning for Recommender Systems/070 [Activity] Recommendations with RBMs part 1-en.srt 25KB
  113. 08 Introduction to Deep Learning [Optional]/052 [Activity] Playing with Tensorflow-en.srt 23KB
  114. 08 Introduction to Deep Learning [Optional]/067 [Activity] Sentiment Analysis of Movie Reviews using RNNs and Keras-en.srt 23KB
  115. 08 Introduction to Deep Learning [Optional]/051 History of Artificial Neural Networks-en.srt 22KB
  116. 08 Introduction to Deep Learning [Optional]/059 [Activity] Handwriting Recognition with Keras-en.srt 20KB
  117. 06 Neighborhood-Based Collaborative Filtering/031 Similarity Metrics-en.srt 19KB
  118. 08 Introduction to Deep Learning [Optional]/062 Intro to Convolutional Neural Networks (CNNs)-en.srt 18KB
  119. 08 Introduction to Deep Learning [Optional]/061 [Exercise] Predict Political Parties of Politicians with Keras-en.srt 18KB
  120. 08 Introduction to Deep Learning [Optional]/050 Deep Learning Pre-Requisites-en.srt 18KB
  121. 05 Content-Based Filtering/025 Content-Based Recommendations and the Cosine Similarity Metric-en.srt 18KB
  122. 08 Introduction to Deep Learning [Optional]/064 [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs)-en.srt 17KB
  123. 09 Deep Learning for Recommender Systems/079 Exercise Results GRU4Rec in Action-en.srt 16KB
  124. 09 Deep Learning for Recommender Systems/069 Restricted Boltzmann Machines (RBMs)-en.srt 16KB
  125. 08 Introduction to Deep Learning [Optional]/065 Intro to Recurrent Neural Networks (RNNs)-en.srt 16KB
  126. 01 Getting Started/002 [Activity] Install Anaconda course materials and create movie recommendations-en.srt 15KB
  127. 09 Deep Learning for Recommender Systems/077 Clickstream Recommendations with RNNs-en.srt 15KB
  128. 04 A Recommender Engine Framework/021 Our Recommender Engine Architecture-en.srt 15KB
  129. 06 Neighborhood-Based Collaborative Filtering/032 User-based Collaborative Filtering-en.srt 14KB
  130. 12 Case Studies/103 Case Study YouTube Part 2-en.srt 14KB
  131. 09 Deep Learning for Recommender Systems/071 [Activity] Recommendations with RBMs part 2-en.srt 14KB
  132. 07 Matrix Factorization Methods/043 Principal Component Analysis (PCA)-en.srt 14KB
  133. 07 Matrix Factorization Methods/044 Singular Value Decomposition-en.srt 14KB
  134. 08 Introduction to Deep Learning [Optional]/057 [Activity] Handwriting Recognition with Tensorflow part 2-en.srt 13KB
  135. 09 Deep Learning for Recommender Systems/076 [Activity] Recommendations with Deep Neural Networks-en.srt 13KB
  136. 11 Real-World Challenges of Recommender Systems/091 The Cold Start Problem (and solutions)-en.srt 13KB
  137. 10 Scaling it Up/090 SageMaker in Action Factorization Machines on one million ratings in the cloud-en.srt 13KB
  138. 03 Evaluating Recommender Systems/018 [Activity] Walkthrough of RecommenderMetrics.py-en.srt 13KB
  139. 08 Introduction to Deep Learning [Optional]/053 Training Neural Networks-en.srt 12KB
  140. 11 Real-World Challenges of Recommender Systems/097 Filter Bubbles Trust and Outliers-en.srt 12KB
  141. 10 Scaling it Up/087 DSSTNE in Action-en.srt 12KB
  142. 01 Getting Started/006 Top-N Recommender Architecture-en.srt 12KB
  143. 09 Deep Learning for Recommender Systems/080 Bleeding Edge Alert Deep Factorization Machines-en.srt 11KB
  144. 10 Scaling it Up/084 [Activity] Movie Recommendations with Spark Matrix Factorization and ALS-en.srt 11KB
  145. 03 Evaluating Recommender Systems/016 Churn Responsiveness and AB Tests-en.srt 11KB
  146. 06 Neighborhood-Based Collaborative Filtering/030 Measuring Similarity and Sparsity-en.srt 11KB
  147. 03 Evaluating Recommender Systems/019 [Activity] Walkthrough of TestMetrics.py-en.srt 11KB
  148. 09 Deep Learning for Recommender Systems/081 More Emerging Tech to Watch-en.srt 11KB
  149. 03 Evaluating Recommender Systems/015 Coverage Diversity and Novelty-en.srt 11KB
  150. 05 Content-Based Filtering/027 [Activity] Producing and Evaluating Content-Based Movie Recommendations-en.srt 10KB
  151. 11 Real-World Challenges of Recommender Systems/094 Stoplists-en.srt 10KB
  152. 10 Scaling it Up/083 Apache Spark Architecture-en.srt 10KB
  153. 11 Real-World Challenges of Recommender Systems/100 Fraud The Perils of Clickstream and International Concerns-en.srt 10KB
  154. 06 Neighborhood-Based Collaborative Filtering/033 [Activity] User-based Collaborative Filtering Hands-On-en.srt 10KB
  155. 02 Introduction to Python [Optional]/009 Data Structures in Python-en.srt 9KB
  156. 09 Deep Learning for Recommender Systems/075 Auto-Encoders for Recommendations Deep Learning for Recs-en.srt 9KB
  157. 10 Scaling it Up/086 Amazon DSSTNE-en.srt 9KB
  158. 03 Evaluating Recommender Systems/014 Top-N Hit Rate - Many Ways-en.srt 9KB
  159. 07 Matrix Factorization Methods/046 Improving on SVD-en.srt 9KB
  160. 01 Getting Started/007 [Quiz] Review the basics of recommender systems.-en.srt 9KB
  161. 06 Neighborhood-Based Collaborative Filtering/034 Item-based Collaborative Filtering-en.srt 9KB
  162. 06 Neighborhood-Based Collaborative Filtering/041 [Exercise] Experiment with different KNN parameters.-en.srt 9KB
  163. 01 Getting Started/005 Understanding You through Implicit and Explicit Ratings-en.srt 9KB
  164. 02 Introduction to Python [Optional]/008 [Activity] The Basics of Python-en.srt 9KB
  165. 05 Content-Based Filtering/028 [Activity] Bleeding Edge Alert Mise en Scene Recommendations-en.srt 9KB
  166. 05 Content-Based Filtering/029 [Exercise] Dive Deeper into Content-Based Recommendations-en.srt 8KB
  167. 03 Evaluating Recommender Systems/013 Accuracy Metrics (RMSE MAE)-en.srt 8KB
  168. 10 Scaling it Up/089 AWS SageMaker and Factorization Machines-en.srt 8KB
  169. 10 Scaling it Up/085 [Activity] Recommendations from 20 million ratings with Spark-en.srt 8KB
  170. 13 Hybrid Approaches/107 Exercise Solution Hybrid Recommenders-en.srt 8KB
  171. 03 Evaluating Recommender Systems/012 TrainTest and Cross Validation-en.srt 8KB
  172. 01 Getting Started/003 Course Roadmap-en.srt 8KB
  173. 08 Introduction to Deep Learning [Optional]/054 Tuning Neural Networks-en.srt 8KB
  174. 06 Neighborhood-Based Collaborative Filtering/039 KNN Recommenders-en.srt 8KB
  175. 10 Scaling it Up/082 [Activity] Introduction and Installation of Apache Spark-en.srt 8KB
  176. 05 Content-Based Filtering/026 K-Nearest-Neighbors and Content Recs-en.srt 8KB
  177. 08 Introduction to Deep Learning [Optional]/060 Classifier Patterns with Keras-en.srt 8KB
  178. 04 A Recommender Engine Framework/023 [Activity] Recommender Engine Walkthrough Part 2-en.srt 8KB
  179. 12 Case Studies/104 Case Study Netflix Part 1-en.srt 8KB
  180. 12 Case Studies/105 Case Study Netflix Part 2-en.srt 8KB
  181. 07 Matrix Factorization Methods/048 Bleeding Edge Alert Sparse Linear Methods (SLIM)-en.srt 8KB
  182. 11 Real-World Challenges of Recommender Systems/099 Exercise Solution Outlier Removal-en.srt 8KB
  183. 11 Real-World Challenges of Recommender Systems/101 Temporal Effects and Value-Aware Recommendations-en.srt 8KB
  184. 04 A Recommender Engine Framework/022 [Activity] Recommender Engine Walkthrough Part 1-en.srt 7KB
  185. 12 Case Studies/102 Case Study YouTube Part 1-en.srt 7KB
  186. 06 Neighborhood-Based Collaborative Filtering/036 [Exercise] Tuning Collaborative Filtering Algorithms-en.srt 7KB
  187. 01 Getting Started/004 Types of Recommenders-en.srt 7KB
  188. 09 Deep Learning for Recommender Systems/072 [Activity] Evaluating the RBM Recommender-en.srt 7KB
  189. 08 Introduction to Deep Learning [Optional]/066 Training Recurrent Neural Networks-en.srt 7KB
  190. 07 Matrix Factorization Methods/045 [Activity] Running SVD and SVD on MovieLens-en.srt 6KB
  191. 04 A Recommender Engine Framework/024 [Activity] Review the Results of our Algorithm Evaluation.-en.srt 6KB
  192. 02 Introduction to Python [Optional]/011 [Exercise] Booleans loops and a hands-on challenge-en.srt 6KB
  193. 08 Introduction to Deep Learning [Optional]/063 CNN Architectures-en.srt 6KB
  194. 08 Introduction to Deep Learning [Optional]/058 Introduction to Keras-en.srt 6KB
  195. 13 Hybrid Approaches/106 Hybrid Recommenders and Exercise-en.srt 6KB
  196. 03 Evaluating Recommender Systems/017 [Quiz] Review ways to measure your recommender.-en.srt 5KB
  197. 09 Deep Learning for Recommender Systems/078 [Exercise] Get GRU4Rec Working on your Desktop-en.srt 5KB
  198. 02 Introduction to Python [Optional]/010 Functions in Python-en.srt 5KB
  199. 03 Evaluating Recommender Systems/020 [Activity] Measure the Performance of SVD Recommendations-en.srt 5KB
  200. 14 Wrapping Up/108 More to Explore-en.srt 5KB
  201. 06 Neighborhood-Based Collaborative Filtering/042 Bleeding Edge Alert Translation-Based Recommendations-en.srt 5KB
  202. 06 Neighborhood-Based Collaborative Filtering/035 [Activity] Item-based Collaborative Filtering Hands-On-en.srt 5KB
  203. 09 Deep Learning for Recommender Systems/068 Intro to Deep Learning for Recommenders-en.srt 5KB
  204. 06 Neighborhood-Based Collaborative Filtering/040 [Activity] Running User and Item-Based KNN on MovieLens-en.srt 5KB
  205. 06 Neighborhood-Based Collaborative Filtering/038 [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering-en.srt 4KB
  206. 11 Real-World Challenges of Recommender Systems/096 Exercise Solution Implement a Stoplist-en.srt 4KB
  207. 10 Scaling it Up/088 Scaling Up DSSTNE-en.srt 4KB
  208. 11 Real-World Challenges of Recommender Systems/093 Exercise Solution Random Exploration-en.srt 4KB
  209. 07 Matrix Factorization Methods/047 [Exercise] Tune the hyperparameters on SVD-en.srt 4KB
  210. 01 Getting Started/001 Udemy 101 Getting the Most From This Course-en.srt 4KB
  211. 09 Deep Learning for Recommender Systems/073 [Exercise] Tuning Restricted Boltzmann Machines-en.srt 4KB
  212. 08 Introduction to Deep Learning [Optional]/049 Deep Learning Introduction-en.srt 3KB
  213. 06 Neighborhood-Based Collaborative Filtering/037 [Activity] Evaluating Collaborative Filtering Systems Offline-en.srt 3KB
  214. 09 Deep Learning for Recommender Systems/074 Exercise Results Tuning a RBM Recommender-en.srt 2KB
  215. 11 Real-World Challenges of Recommender Systems/092 [Exercise] Implement Random Exploration-en.srt 2KB
  216. 14 Wrapping Up/109 Bonus Lecture Companion Book and More Courses from Sundog Education-en.srt 2KB
  217. 11 Real-World Challenges of Recommender Systems/098 [Exercise] Identify and Eliminate Outlier Users-en.srt 2KB
  218. 11 Real-World Challenges of Recommender Systems/095 [Exercise] Implement a Stoplist-en.srt 1KB
  219. udemycoursedownloader.com.url 132B
  220. Udemy Course downloader.txt 94B
  221. 14 Wrapping Up/109 Sundog-Education-website.txt 35B
  222. 14 Wrapping Up/109 Building-Recommender-Systems-book-on-Amazon.txt 23B