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1. Content-Based Recommendations, and the Cosine Similarity Metric.mp4
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1. Measuring Similarity, and Sparsity.mp4
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1. Principal Component Analysis (PCA).mp4
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1. The Cold Start Problem (and solutions).mp4
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1. Udemy 101 Getting the Most From This Course.mp4
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1. [Activity] Introduction and Installation of Apache Spark.mp4
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1. [Activity] The Basics of Python.mp4
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10. Clickstream Recommendations with RNN's.mp4
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10. KNN Recommenders.mp4
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10. [Activity] Handwriting Recognition with Tensorflow, Part 3.mp4
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11. Introduction to Keras.mp4
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11. [Activity] Running User and Item-Based KNN on MovieLens.mp4
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11. [Exercise] Get GRU4Rec Working on your Desktop.mp4
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12. [Activity] Handwriting Recognition with Keras.mp4
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12. [Exercise] Experiment with different KNN parameters..mp4
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13. Bleeding Edge Alert! Deep Factorization Machines.mp4
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13. Bleeding Edge Alert! Translation-Based Recommendations.mp4
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13. Classifier Patterns with Keras.mp4
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14. More Emerging Tech to Watch.mp4
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14. [Exercise] Predict Political Parties of Politicians with Keras.mp4
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14. [Exercise] Predict Political Parties of Politicians with Keras.srt
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15. Intro to Convolutional Neural Networks (CNN's).mp4
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16. CNN Architectures.mp4
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17. [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp4
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17. [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).srt
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18. Intro to Recurrent Neural Networks (RNN's).mp4
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19. Training Recurrent Neural Networks.mp4
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2. Accuracy Metrics (RMSE, MAE).mp4
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2. Apache Spark Architecture.mp4
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2. Bonus Lecture Discounts to continue your journey!.html
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2. Case Study YouTube, Part 2.mp4
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2. Data Structures in Python.mp4
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2. Deep Learning Pre-Requisites.mp4
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2. Exercise Solution Hybrid Recommenders.mp4
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2. K-Nearest-Neighbors and Content Recs.mp4
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2. Restricted Boltzmann Machines (RBM's).mp4
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2. Similarity Metrics.mp4
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2. Singular Value Decomposition.mp4
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2. [Activity] Install Anaconda, course materials, and create movie recommendations!.mp4
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2. [Activity] Install Anaconda, course materials, and create movie recommendations!.srt
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2. [Activity] Recommender Engine Walkthrough, Part 1.mp4
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2. [Exercise] Implement Random Exploration.mp4
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20. [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras.mp4
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20. [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras.srt
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3. Case Study Netflix, Part 1.mp4
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3. Course Roadmap.mp4
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3. Exercise Solution Random Exploration.mp4
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3. Functions in Python.mp4
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3. History of Artificial Neural Networks.mp4
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3. Top-N Hit Rate - Many Ways.mp4
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3. User-based Collaborative Filtering.mp4
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3. [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.mp4
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3. [Activity] Producing and Evaluating Content-Based Movie Recommendations.mp4
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3. [Activity] Recommendations with RBM's, part 1.mp4
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3. [Activity] Recommender Engine Walkthrough, Part 2.mp4
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3. [Activity] Running SVD and SVD++ on MovieLens.mp4
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4. Case Study Netflix, Part 2.mp4
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4. Coverage, Diversity, and Novelty.mp4
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4. Stoplists.mp4
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4. Types of Recommenders.mp4
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4. [Activity] Bleeding Edge Alert! Mise en Scene Recommendations.mp4
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4. [Activity] Playing with Tensorflow.mp4
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4. [Activity] Recommendations from 20 million ratings with Spark.mp4
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4. [Activity] Recommendations with RBM's, part 2.mp4
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4. [Activity] Review the Results of our Algorithm Evaluation..mp4
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4. [Activity] User-based Collaborative Filtering, Hands-On.mp4
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4. [Activity] User-based Collaborative Filtering, Hands-On.srt
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4. [Exercise] Booleans, loops, and a hands-on challenge.mp4
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13.9 MB
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4. [Exercise] Booleans, loops, and a hands-on challenge.srt
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5. Amazon DSSTNE.mp4
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5. Amazon DSSTNE.srt
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5. Churn, Responsiveness, and AB Tests.mp4
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5. Item-based Collaborative Filtering.mp4
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5. Item-based Collaborative Filtering.srt
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5. Training Neural Networks.mp4
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5. Training Neural Networks.srt
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5. Understanding You through Implicit and Explicit Ratings.mp4
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5. [Activity] Evaluating the RBM Recommender.mp4
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5. [Exercise] Dive Deeper into Content-Based Recommendations.mp4
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24.1 MB
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5. [Exercise] Implement a Stoplist.mp4
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5. [Exercise] Tune the hyperparameters on SVD.mp4
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5. [Exercise] Tune the hyperparameters on SVD.srt
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6. Bleeding Edge Alert! Sparse Linear Methods (SLIM).mp4
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6. Bleeding Edge Alert! Sparse Linear Methods (SLIM).srt
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6. DSSTNE in Action.mp4
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113.7 MB
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6. DSSTNE in Action.srt
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6. Exercise Solution Implement a Stoplist.mp4
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26.7 MB
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6. Exercise Solution Implement a Stoplist.srt
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6. Top-N Recommender Architecture.mp4
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37.1 MB
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6. Top-N Recommender Architecture.srt
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12.8 KB
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6. Tuning Neural Networks.mp4
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31 MB
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6. Tuning Neural Networks.srt
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6. [Activity] Item-based Collaborative Filtering, Hands-On.mp4
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26.8 MB
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6. [Activity] Item-based Collaborative Filtering, Hands-On.srt
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6. [Exercise] Tuning Restricted Boltzmann Machines.mp4
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6. [Quiz] Review ways to measure your recommender..mp4
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6. [Quiz] Review ways to measure your recommender..srt
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7. Exercise Results Tuning a RBM Recommender.mp4
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7. Exercise Results Tuning a RBM Recommender.srt
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7. Filter Bubbles, Trust, and Outliers.mp4
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92.4 MB
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7. Filter Bubbles, Trust, and Outliers.srt
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7. Introduction to Tensorflow.mp4
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92.5 MB
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7. Introduction to Tensorflow.srt
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7. Scaling Up DSSTNE.mp4
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10.4 MB
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7. Scaling Up DSSTNE.srt
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7. [Activity] Walkthrough of RecommenderMetrics.py.mp4
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64.3 MB
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7. [Activity] Walkthrough of RecommenderMetrics.py.srt
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7. [Exercise] Tuning Collaborative Filtering Algorithms.mp4
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19.7 MB
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7. [Exercise] Tuning Collaborative Filtering Algorithms.srt
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7. [Quiz] Review the basics of recommender systems..mp4
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21.3 MB
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7. [Quiz] Review the basics of recommender systems..srt
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8. AWS SageMaker and Factorization Machines.mp4
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15.6 MB
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8. AWS SageMaker and Factorization Machines.srt
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8. Auto-Encoders for Recommendations Deep Learning for Recs.mp4
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26.9 MB
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8. Auto-Encoders for Recommendations Deep Learning for Recs.srt
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8. [Activity] Evaluating Collaborative Filtering Systems Offline.mp4
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15.4 MB
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8. [Activity] Evaluating Collaborative Filtering Systems Offline.srt
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8. [Activity] Handwriting Recognition with Tensorflow, part 1.mp4
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MP4
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91 MB
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8. [Activity] Handwriting Recognition with Tensorflow, part 1.srt
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8. [Activity] Walkthrough of TestMetrics.py.mp4
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MP4
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54.4 MB
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8. [Activity] Walkthrough of TestMetrics.py.srt
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SRT
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11.9 KB
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8. [Exercise] Identify and Eliminate Outlier Users.mp4
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MP4
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1.8 MB
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8. [Exercise] Identify and Eliminate Outlier Users.srt
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2 KB
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9. Exercise Solution Outlier Removal.mp4
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MP4
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38.5 MB
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9. Exercise Solution Outlier Removal.srt
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SRT
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8.3 KB
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9. SageMaker in Action Factorization Machines on one million ratings, in the cloud.mp4
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MP4
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68.3 MB
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9. SageMaker in Action Factorization Machines on one million ratings, in the cloud.srt
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9. [Activity] Handwriting Recognition with Tensorflow, part 2.mp4
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MP4
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90.9 MB
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9. [Activity] Handwriting Recognition with Tensorflow, part 2.srt
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SRT
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9. [Activity] Measure the Performance of SVD Recommendations.mp4
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MP4
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21.6 MB
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9. [Activity] Measure the Performance of SVD Recommendations.srt
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5.5 KB
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9. [Activity] Recommendations with Deep Neural Networks.mp4
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MP4
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75.4 MB
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9. [Activity] Recommendations with Deep Neural Networks.srt
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SRT
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14.7 KB
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9. [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering.mp4
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MP4
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9.5 MB
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9. [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering.srt
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SRT
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ReadMe.txt
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TXT
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204 B
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Visit Coursedrive.org.url
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URL
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102 B
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![Udemy - Building Recommender Systems with Machine Learning and AI [Course Drive]](/upload_files/resize_cache/torrents-posters/cc7/174_260_2/cc746ec07e3357aedecd689afa48ab2c.jpg "Udemy - Building Recommender Systems with Machine Learning and AI [Course Drive]")
