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[] Packtpub - Building Recommender Systems with Machine Learning and AI

  • 收录时间:2018-12-17 02:30:41
  • 文件大小:3GB
  • 下载次数:105
  • 最近下载:2021-01-17 21:13:42
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文件列表

  1. 08.Introduction to Deep Learning/0804.[Activity] Playing with Tensorflow.mp4 117MB
  2. 08.Introduction to Deep Learning/0808.[Activity] Handwriting Recognition with Tensorflow, part 1.mp4 93MB
  3. 01.Getting Started/0101.Install Anaconda, course materials, and create movie recommendations!.mp4 88MB
  4. 03.Evaluating Recommender Systems/0305.Churn, Responsiveness, and AB Tests.mp4 83MB
  5. 11.11 Real-World Challenges of Recommender Systems/1111.Temporal Effects, and Value-Aware Recommendations.mp4 82MB
  6. 08.Introduction to Deep Learning/0819.[Activity] Sentiment Analysis of Movie Reviews using RNN_s and Keras.mp4 73MB
  7. 11.11 Real-World Challenges of Recommender Systems/1110.Fraud, the Perils of Clickstream, and International Concerns.mp4 73MB
  8. 06.Neighborhood-Based Collaborative Filtering/0601.Measuring Similarity, and Sparsity.mp4 70MB
  9. 01.Getting Started/0102.Course Roadmap.mp4 69MB
  10. 07.Matrix Factorization Methods/0701.Principal Component Analysis (PCA).mp4 65MB
  11. 14.Wrapping Up/1401.More to Explore.mp4 62MB
  12. 06.Neighborhood-Based Collaborative Filtering/0605.Item-based Collaborative Filtering.mp4 62MB
  13. 10.Scaling it up/1006.DSSTNE in Action.mp4 61MB
  14. 09.Deep Learning for Recommender Systems/0901.Intro to Deep Learning for Recommenders.mp4 56MB
  15. 09.Deep Learning for Recommender Systems/0906.[Exercise] Tuning Restricted Boltzmann Machines.mp4 54MB
  16. 08.Introduction to Deep Learning/0813.[Exercise] Predict Political Parties of Politicians with Keras.mp4 54MB
  17. 09.Deep Learning for Recommender Systems/0903.[Activity] Recommendations with RBM_s, part 1.mp4 51MB
  18. 08.Introduction to Deep Learning/0811.[Activity] Handwriting Recognition with Keras.mp4 47MB
  19. 03.Evaluating Recommender Systems/0302.Accuracy Metrics (RMSE, MAE).mp4 47MB
  20. 09.Deep Learning for Recommender Systems/0913.Bleeding Edge Alert! Deep Factorization Machines.mp4 44MB
  21. 10.Scaling it up/1009.SageMaker in Action Factorization Machines on one million ratings, in the cloud.mp4 44MB
  22. 08.Introduction to Deep Learning/0807.Introduction to Tensorflow.mp4 43MB
  23. 08.Introduction to Deep Learning/0816.[Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp4 42MB
  24. 02.Introduction to Python/0201.The Basics of Python.mp4 42MB
  25. 10.Scaling it up/1005.Amazon DSSTNE.mp4 41MB
  26. 09.Deep Learning for Recommender Systems/0912.Exercise Results GRU4Rec in Action.mp4 41MB
  27. 08.Introduction to Deep Learning/0803.History of Artificial Neural Networks.mp4 40MB
  28. 10.Scaling it up/1001.[Activity] Introduction and Installation of Apache Spark.mp4 40MB
  29. 03.Evaluating Recommender Systems/0307.Walkthrough of RecommenderMetrics.py.mp4 39MB
  30. 06.Neighborhood-Based Collaborative Filtering/0612.Experiment with different KNN parameters..mp4 39MB
  31. 05.Content-Based Filtering/0501.Content-Based Recommendations, and the Cosine Similarity Metric.mp4 38MB
  32. 09.Deep Learning for Recommender Systems/0909.[Activity] Recommendations with Deep Neural Networks.mp4 37MB
  33. 08.Introduction to Deep Learning/0814.Intro to Convolutional Neural Networks (CNN_s).mp4 36MB
  34. 05.Content-Based Filtering/0504.Bleeding Edge Alert! Mise en Scene Recommendations.mp4 34MB
  35. 05.Content-Based Filtering/0503.Producing and Evaluating Content-Based Movie Recommendations.mp4 28MB
  36. 08.Introduction to Deep Learning/0809.[Activity] Handwriting Recognition with Tensorflow, part 2.mp4 27MB
  37. 10.Scaling it up/1004.[Activity] Recommendations from 20 million ratings with Spark.mp4 27MB
  38. 09.Deep Learning for Recommender Systems/0904.[Activity] Recommendations with RBM_s, part 2.mp4 26MB
  39. 03.Evaluating Recommender Systems/0308.Walkthrough of TestMetrics.py.mp4 25MB
  40. 09.Deep Learning for Recommender Systems/0910.Clickstream Recommendations with RNN_s.mp4 25MB
  41. 06.Neighborhood-Based Collaborative Filtering/0604.User-based Collaborative Filtering, Hands-On.mp4 25MB
  42. 10.Scaling it up/1003.[Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.mp4 24MB
  43. 03.Evaluating Recommender Systems/0301.TrainTest and Cross Validation.mp4 23MB
  44. 07.Matrix Factorization Methods/0703.Running SVD and SVD++ on MovieLens.mp4 23MB
  45. 08.Introduction to Deep Learning/0801.Deep Learning Introduction.mp4 23MB
  46. 08.Introduction to Deep Learning/0817.Intro to Recurrent Neural Networks (RNN_s).mp4 22MB
  47. 06.Neighborhood-Based Collaborative Filtering/0610.KNN Recommenders.mp4 22MB
  48. 11.11 Real-World Challenges of Recommender Systems/1107.Filter Bubbles, Trust, and Outliers.mp4 22MB
  49. 07.Matrix Factorization Methods/0706.Bleeding Edge Alert! Sparse Linear Methods (SLIM).mp4 21MB
  50. 13.Hybrid Approaches/1302.Exercise Solution Hybrid Recommenders.mp4 20MB
  51. 08.Introduction to Deep Learning/0802.Deep Learning Pre-Requisites.mp4 20MB
  52. 06.Neighborhood-Based Collaborative Filtering/0603.User-based Collaborative Filtering.mp4 20MB
  53. 09.Deep Learning for Recommender Systems/0905.[Activity] Evaluating the RBM Recommender.mp4 20MB
  54. 06.Neighborhood-Based Collaborative Filtering/0613.Bleeding Edge Alert! Translation-Based Recommendations.mp4 20MB
  55. 06.Neighborhood-Based Collaborative Filtering/0611.Running User and Item-Based KNN on MovieLens.mp4 20MB
  56. 08.Introduction to Deep Learning/0805.Training Neural Networks.mp4 19MB
  57. 04.A Recommender Engine Framework/0403.Recommender Engine Walkthrough, Part 2.mp4 19MB
  58. 04.A Recommender Engine Framework/0402.Recommender Engine Walkthrough, Part 1.mp4 19MB
  59. 04.A Recommender Engine Framework/0401.Our Recommender Engine Architecture.mp4 18MB
  60. 06.Neighborhood-Based Collaborative Filtering/0606.Item-based Collaborative Filtering, Hands-On.mp4 18MB
  61. 11.11 Real-World Challenges of Recommender Systems/1109.Exercise Solution Outlier Removal.mp4 17MB
  62. 09.Deep Learning for Recommender Systems/0902.Restricted Boltzmann Machines (RBM_s).mp4 16MB
  63. 06.Neighborhood-Based Collaborative Filtering/0602.Similarity Metrics.mp4 15MB
  64. 11.11 Real-World Challenges of Recommender Systems/1103.Exercise Solution Random Exploration.mp4 15MB
  65. 01.Getting Started/0105.Top-N Recommender Architecture.mp4 15MB
  66. 11.11 Real-World Challenges of Recommender Systems/1106.Exercise Solution Implement a Stoplist.mp4 15MB
  67. 04.A Recommender Engine Framework/0404.Review the Results of our Algorithm Evaluation..mp4 14MB
  68. 09.Deep Learning for Recommender Systems/0914.More Emerging Tech to Watch.mp4 14MB
  69. 01.Getting Started/0103.Types of Recommenders.mp4 14MB
  70. 12.Case Studies/1203.Case Study Netflix, Part 1.mp4 14MB
  71. 08.Introduction to Deep Learning/0812.Classifier Patterns with Keras.mp4 13MB
  72. 08.Introduction to Deep Learning/0806.Tuning Neural Networks.mp4 13MB
  73. 07.Matrix Factorization Methods/0702.Singular Value Decomposition.mp4 13MB
  74. 12.Case Studies/1201.Case Study YouTube, Part 1.mp4 13MB
  75. 12.Case Studies/1202.Case Study YouTube, Part 2.mp4 12MB
  76. 03.Evaluating Recommender Systems/0303.Top-N Hit Rate - Many Ways.mp4 12MB
  77. 03.Evaluating Recommender Systems/0309.Measure the Performance of SVD Recommendations.mp4 12MB
  78. 05.Content-Based Filtering/0502.K-Nearest-Neighbors and Content Recs.mp4 12MB
  79. 09.Deep Learning for Recommender Systems/0908.Auto-Encoders for Recommendations Deep Learning for Recs.mp4 12MB
  80. 11.11 Real-World Challenges of Recommender Systems/1101.The Cold Start Problem (and solutions).mp4 12MB
  81. 02.Introduction to Python/0202.Data Structures in Python.mp4 12MB
  82. 01.Getting Started/0106.Review the basics of recommender systems..mp4 11MB
  83. 05.Content-Based Filtering/0505.Dive Deeper into Content-Based Recommendations.mp4 11MB
  84. 06.Neighborhood-Based Collaborative Filtering/0608.Evaluating Collaborative Filtering Systems Offline.mp4 11MB
  85. 08.Introduction to Deep Learning/0818.Training Recurrent Neural Networks.mp4 10MB
  86. 06.Neighborhood-Based Collaborative Filtering/0607.Tuning Collaborative Filtering Algorithms.mp4 10MB
  87. 12.Case Studies/1204.Case Study Netflix, Part 2.mp4 10MB
  88. 07.Matrix Factorization Methods/0704.Improving on SVD.mp4 10MB
  89. 08.Introduction to Deep Learning/0815.CNN Architectures.mp4 10MB
  90. 10.Scaling it up/1002.Apache Spark Architecture.mp4 9MB
  91. 01.Getting Started/0104.Understanding You through Implicit and Explicit Ratings.mp4 9MB
  92. 13.Hybrid Approaches/1301.Hybrid Recommenders and Exercise.mp4 9MB
  93. 11.11 Real-World Challenges of Recommender Systems/1104.Stoplists.mp4 9MB
  94. 03.Evaluating Recommender Systems/0306.Review ways to measure your recommender..mp4 8MB
  95. 07.Matrix Factorization Methods/0705.Tune the hyperparameters on SVD.mp4 8MB
  96. 10.Scaling it up/1008.AWS SageMaker and Factorization Machines.mp4 8MB
  97. 03.Evaluating Recommender Systems/0304.Coverage, Diversity, and Novelty.mp4 8MB
  98. 02.Introduction to Python/0204.Booleans, loops, and a hands-on challenge.mp4 7MB
  99. 08.Introduction to Deep Learning/0810.Introduction to Keras.mp4 7MB
  100. 09.Deep Learning for Recommender Systems/0907.Exercise Results Tuning a RBM Recommender.mp4 7MB
  101. 02.Introduction to Python/0203.Functions in Python.mp4 6MB
  102. 10.Scaling it up/1007.Scaling Up DSSTNE.mp4 5MB
  103. 06.Neighborhood-Based Collaborative Filtering/0609.Measure the Hit Rate of Item-Based Collaborative Filtering.mp4 4MB
  104. 09.Deep Learning for Recommender Systems/0911.[Exercise] Get GRU4Rec Working on your Desktop.mp4 4MB
  105. Exercise Files/exercise_files.zip 2MB
  106. 11.11 Real-World Challenges of Recommender Systems/1102.[Exercise] Implement Random Exploration.mp4 1MB
  107. 11.11 Real-World Challenges of Recommender Systems/1108.[Exercise] Identify and Eliminate Outlier Users.mp4 1020KB
  108. 11.11 Real-World Challenges of Recommender Systems/1105.[Exercise] Implement a Stoplist.mp4 762KB
  109. [CourseClub.NET].url 123B
  110. [DesireCourse.Com].url 51B