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文档列表

08.Introduction to Deep Learning/0804.[Activity] Playing with Tensorflow.mp4 122.6 MB
08.Introduction to Deep Learning/0808.[Activity] Handwriting Recognition with Tensorflow, part 1.mp4 97.4 MB
01.Getting Started/0101.Install Anaconda, course materials, and create movie recommendations!.mp4 92.4 MB
03.Evaluating Recommender Systems/0305.Churn, Responsiveness, and AB Tests.mp4 86.7 MB
11.11 Real-World Challenges of Recommender Systems/1111.Temporal Effects, and Value-Aware Recommendations.mp4 85.6 MB
08.Introduction to Deep Learning/0819.[Activity] Sentiment Analysis of Movie Reviews using RNN_s and Keras.mp4 76.9 MB
11.11 Real-World Challenges of Recommender Systems/1110.Fraud, the Perils of Clickstream, and International Concerns.mp4 76.3 MB
06.Neighborhood-Based Collaborative Filtering/0601.Measuring Similarity, and Sparsity.mp4 73.1 MB
01.Getting Started/0102.Course Roadmap.mp4 72.6 MB
07.Matrix Factorization Methods/0701.Principal Component Analysis (PCA).mp4 68.1 MB
14.Wrapping Up/1401.More to Explore.mp4 64.9 MB
06.Neighborhood-Based Collaborative Filtering/0605.Item-based Collaborative Filtering.mp4 64.6 MB
10.Scaling it up/1006.DSSTNE in Action.mp4 64.1 MB
09.Deep Learning for Recommender Systems/0901.Intro to Deep Learning for Recommenders.mp4 58.7 MB
09.Deep Learning for Recommender Systems/0906.[Exercise] Tuning Restricted Boltzmann Machines.mp4 56.3 MB
08.Introduction to Deep Learning/0813.[Exercise] Predict Political Parties of Politicians with Keras.mp4 56.3 MB
09.Deep Learning for Recommender Systems/0903.[Activity] Recommendations with RBM_s, part 1.mp4 53.0 MB
08.Introduction to Deep Learning/0811.[Activity] Handwriting Recognition with Keras.mp4 49.2 MB
03.Evaluating Recommender Systems/0302.Accuracy Metrics (RMSE, MAE).mp4 49.0 MB
09.Deep Learning for Recommender Systems/0913.Bleeding Edge Alert! Deep Factorization Machines.mp4 46.5 MB
10.Scaling it up/1009.SageMaker in Action Factorization Machines on one million ratings, in the cloud.mp4 46.3 MB
08.Introduction to Deep Learning/0807.Introduction to Tensorflow.mp4 45.1 MB
08.Introduction to Deep Learning/0816.[Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp4 44.5 MB
02.Introduction to Python/0201.The Basics of Python.mp4 44.0 MB
10.Scaling it up/1005.Amazon DSSTNE.mp4 43.4 MB
09.Deep Learning for Recommender Systems/0912.Exercise Results GRU4Rec in Action.mp4 43.0 MB
08.Introduction to Deep Learning/0803.History of Artificial Neural Networks.mp4 42.4 MB
10.Scaling it up/1001.[Activity] Introduction and Installation of Apache Spark.mp4 42.0 MB
03.Evaluating Recommender Systems/0307.Walkthrough of RecommenderMetrics.py.mp4 40.7 MB
06.Neighborhood-Based Collaborative Filtering/0612.Experiment with different KNN parameters..mp4 40.7 MB
05.Content-Based Filtering/0501.Content-Based Recommendations, and the Cosine Similarity Metric.mp4 40.3 MB
09.Deep Learning for Recommender Systems/0909.[Activity] Recommendations with Deep Neural Networks.mp4 39.0 MB
08.Introduction to Deep Learning/0814.Intro to Convolutional Neural Networks (CNN_s).mp4 38.2 MB
05.Content-Based Filtering/0504.Bleeding Edge Alert! Mise en Scene Recommendations.mp4 35.3 MB
05.Content-Based Filtering/0503.Producing and Evaluating Content-Based Movie Recommendations.mp4 29.2 MB
08.Introduction to Deep Learning/0809.[Activity] Handwriting Recognition with Tensorflow, part 2.mp4 28.7 MB
10.Scaling it up/1004.[Activity] Recommendations from 20 million ratings with Spark.mp4 28.2 MB
09.Deep Learning for Recommender Systems/0904.[Activity] Recommendations with RBM_s, part 2.mp4 27.7 MB
03.Evaluating Recommender Systems/0308.Walkthrough of TestMetrics.py.mp4 26.6 MB
09.Deep Learning for Recommender Systems/0910.Clickstream Recommendations with RNN_s.mp4 26.1 MB
06.Neighborhood-Based Collaborative Filtering/0604.User-based Collaborative Filtering, Hands-On.mp4 25.8 MB
10.Scaling it up/1003.[Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.mp4 24.9 MB
03.Evaluating Recommender Systems/0301.TrainTest and Cross Validation.mp4 24.3 MB
07.Matrix Factorization Methods/0703.Running SVD and SVD++ on MovieLens.mp4 24.2 MB
08.Introduction to Deep Learning/0801.Deep Learning Introduction.mp4 23.9 MB
08.Introduction to Deep Learning/0817.Intro to Recurrent Neural Networks (RNN_s).mp4 23.6 MB
06.Neighborhood-Based Collaborative Filtering/0610.KNN Recommenders.mp4 22.9 MB
11.11 Real-World Challenges of Recommender Systems/1107.Filter Bubbles, Trust, and Outliers.mp4 22.8 MB
07.Matrix Factorization Methods/0706.Bleeding Edge Alert! Sparse Linear Methods (SLIM).mp4 22.1 MB
13.Hybrid Approaches/1302.Exercise Solution Hybrid Recommenders.mp4 21.4 MB
08.Introduction to Deep Learning/0802.Deep Learning Pre-Requisites.mp4 21.1 MB
06.Neighborhood-Based Collaborative Filtering/0603.User-based Collaborative Filtering.mp4 21.0 MB
09.Deep Learning for Recommender Systems/0905.[Activity] Evaluating the RBM Recommender.mp4 20.8 MB
06.Neighborhood-Based Collaborative Filtering/0613.Bleeding Edge Alert! Translation-Based Recommendations.mp4 20.6 MB
06.Neighborhood-Based Collaborative Filtering/0611.Running User and Item-Based KNN on MovieLens.mp4 20.6 MB
08.Introduction to Deep Learning/0805.Training Neural Networks.mp4 19.8 MB
04.A Recommender Engine Framework/0403.Recommender Engine Walkthrough, Part 2.mp4 19.5 MB
04.A Recommender Engine Framework/0402.Recommender Engine Walkthrough, Part 1.mp4 19.5 MB
04.A Recommender Engine Framework/0401.Our Recommender Engine Architecture.mp4 19.1 MB
06.Neighborhood-Based Collaborative Filtering/0606.Item-based Collaborative Filtering, Hands-On.mp4 19.0 MB
11.11 Real-World Challenges of Recommender Systems/1109.Exercise Solution Outlier Removal.mp4 17.4 MB
09.Deep Learning for Recommender Systems/0902.Restricted Boltzmann Machines (RBM_s).mp4 16.7 MB
06.Neighborhood-Based Collaborative Filtering/0602.Similarity Metrics.mp4 16.2 MB
11.11 Real-World Challenges of Recommender Systems/1103.Exercise Solution Random Exploration.mp4 16.2 MB
01.Getting Started/0105.Top-N Recommender Architecture.mp4 16.1 MB
11.11 Real-World Challenges of Recommender Systems/1106.Exercise Solution Implement a Stoplist.mp4 15.8 MB
04.A Recommender Engine Framework/0404.Review the Results of our Algorithm Evaluation..mp4 15.0 MB
09.Deep Learning for Recommender Systems/0914.More Emerging Tech to Watch.mp4 14.9 MB
01.Getting Started/0103.Types of Recommenders.mp4 14.8 MB
12.Case Studies/1203.Case Study Netflix, Part 1.mp4 14.5 MB
08.Introduction to Deep Learning/0812.Classifier Patterns with Keras.mp4 13.8 MB
08.Introduction to Deep Learning/0806.Tuning Neural Networks.mp4 13.7 MB
07.Matrix Factorization Methods/0702.Singular Value Decomposition.mp4 13.6 MB
12.Case Studies/1201.Case Study YouTube, Part 1.mp4 13.4 MB
12.Case Studies/1202.Case Study YouTube, Part 2.mp4 13.1 MB
03.Evaluating Recommender Systems/0303.Top-N Hit Rate - Many Ways.mp4 12.7 MB
03.Evaluating Recommender Systems/0309.Measure the Performance of SVD Recommendations.mp4 12.6 MB
05.Content-Based Filtering/0502.K-Nearest-Neighbors and Content Recs.mp4 12.4 MB
09.Deep Learning for Recommender Systems/0908.Auto-Encoders for Recommendations Deep Learning for Recs.mp4 12.4 MB
11.11 Real-World Challenges of Recommender Systems/1101.The Cold Start Problem (and solutions).mp4 12.4 MB
02.Introduction to Python/0202.Data Structures in Python.mp4 12.2 MB
01.Getting Started/0106.Review the basics of recommender systems..mp4 11.7 MB
05.Content-Based Filtering/0505.Dive Deeper into Content-Based Recommendations.mp4 11.2 MB
06.Neighborhood-Based Collaborative Filtering/0608.Evaluating Collaborative Filtering Systems Offline.mp4 11.1 MB
08.Introduction to Deep Learning/0818.Training Recurrent Neural Networks.mp4 10.6 MB
06.Neighborhood-Based Collaborative Filtering/0607.Tuning Collaborative Filtering Algorithms.mp4 10.5 MB
12.Case Studies/1204.Case Study Netflix, Part 2.mp4 10.3 MB
07.Matrix Factorization Methods/0704.Improving on SVD.mp4 10.2 MB
08.Introduction to Deep Learning/0815.CNN Architectures.mp4 10.1 MB
10.Scaling it up/1002.Apache Spark Architecture.mp4 9.8 MB
01.Getting Started/0104.Understanding You through Implicit and Explicit Ratings.mp4 9.6 MB
13.Hybrid Approaches/1301.Hybrid Recommenders and Exercise.mp4 9.2 MB
11.11 Real-World Challenges of Recommender Systems/1104.Stoplists.mp4 9.1 MB
03.Evaluating Recommender Systems/0306.Review ways to measure your recommender..mp4 8.7 MB
07.Matrix Factorization Methods/0705.Tune the hyperparameters on SVD.mp4 8.4 MB
10.Scaling it up/1008.AWS SageMaker and Factorization Machines.mp4 8.3 MB
03.Evaluating Recommender Systems/0304.Coverage, Diversity, and Novelty.mp4 8.3 MB
02.Introduction to Python/0204.Booleans, loops, and a hands-on challenge.mp4 7.7 MB
08.Introduction to Deep Learning/0810.Introduction to Keras.mp4 7.0 MB
09.Deep Learning for Recommender Systems/0907.Exercise Results Tuning a RBM Recommender.mp4 7.0 MB
02.Introduction to Python/0203.Functions in Python.mp4 6.1 MB
10.Scaling it up/1007.Scaling Up DSSTNE.mp4 5.0 MB
06.Neighborhood-Based Collaborative Filtering/0609.Measure the Hit Rate of Item-Based Collaborative Filtering.mp4 4.6 MB
09.Deep Learning for Recommender Systems/0911.[Exercise] Get GRU4Rec Working on your Desktop.mp4 4.1 MB
Exercise Files/exercise_files.zip 1.8 MB
11.11 Real-World Challenges of Recommender Systems/1102.[Exercise] Implement Random Exploration.mp4 1.3 MB
11.11 Real-World Challenges of Recommender Systems/1108.[Exercise] Identify and Eliminate Outlier Users.mp4 1.0 MB
11.11 Real-World Challenges of Recommender Systems/1105.[Exercise] Implement a Stoplist.mp4 780.1 kB
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