[UdemyCourseDownloader] Building Recommender Systems with Machine Learning and AI

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[UdemyCourseDownloader] Building Recommender Systems with Machine Learning and AI

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种子哈希：c48a006613228737ff53c9b640b2a698a820ce24
文件大小： 4.48G
已经下载：59次
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收录时间：2024-07-04
最近下载：2024-11-10

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文件列表

  • 08 Introduction to Deep Learning [Optional]/056 [Activity] Handwriting Recognition with Tensorflow part 1.mp4 190.7 MB
  • 08 Introduction to Deep Learning [Optional]/052 [Activity] Playing with Tensorflow.mp4 152.5 MB
  • 09 Deep Learning for Recommender Systems/070 [Activity] Recommendations with RBMs part 1.mp4 151.5 MB
  • 08 Introduction to Deep Learning [Optional]/067 [Activity] Sentiment Analysis of Movie Reviews using RNNs and Keras.mp4 125.6 MB
  • 10 Scaling it Up/087 DSSTNE in Action.mp4 122.3 MB
  • 01 Getting Started/002 [Activity] Install Anaconda course materials and create movie recommendations.mp4 109.1 MB
  • 08 Introduction to Deep Learning [Optional]/061 [Exercise] Predict Political Parties of Politicians with Keras.mp4 105.1 MB
  • 08 Introduction to Deep Learning [Optional]/055 Introduction to Tensorflow.mp4 97.0 MB
  • 11 Real-World Challenges of Recommender Systems/097 Filter Bubbles Trust and Outliers.mp4 96.9 MB
  • 08 Introduction to Deep Learning [Optional]/059 [Activity] Handwriting Recognition with Keras.mp4 93.0 MB
  • 08 Introduction to Deep Learning [Optional]/051 History of Artificial Neural Networks.mp4 88.3 MB
  • 08 Introduction to Deep Learning [Optional]/064 [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp4 86.3 MB
  • 08 Introduction to Deep Learning [Optional]/062 Intro to Convolutional Neural Networks (CNNs).mp4 82.0 MB
  • 09 Deep Learning for Recommender Systems/071 [Activity] Recommendations with RBMs part 2.mp4 80.5 MB
  • 09 Deep Learning for Recommender Systems/076 [Activity] Recommendations with Deep Neural Networks.mp4 79.1 MB
  • 10 Scaling it Up/090 SageMaker in Action Factorization Machines on one million ratings in the cloud.mp4 71.7 MB
  • 03 Evaluating Recommender Systems/018 [Activity] Walkthrough of RecommenderMetrics.py.mp4 67.4 MB
  • 09 Deep Learning for Recommender Systems/079 Exercise Results GRU4Rec in Action.mp4 65.7 MB
  • 05 Content-Based Filtering/025 Content-Based Recommendations and the Cosine Similarity Metric.mp4 64.6 MB
  • 07 Matrix Factorization Methods/043 Principal Component Analysis (PCA).mp4 64.2 MB
  • 03 Evaluating Recommender Systems/016 Churn Responsiveness and AB Tests.mp4 63.9 MB
  • 06 Neighborhood-Based Collaborative Filtering/030 Measuring Similarity and Sparsity.mp4 62.0 MB
  • 11 Real-World Challenges of Recommender Systems/100 Fraud The Perils of Clickstream and International Concerns.mp4 61.1 MB
  • 08 Introduction to Deep Learning [Optional]/057 [Activity] Handwriting Recognition with Tensorflow part 2.mp4 60.4 MB
  • 09 Deep Learning for Recommender Systems/080 Bleeding Edge Alert Deep Factorization Machines.mp4 60.1 MB
  • 10 Scaling it Up/084 [Activity] Movie Recommendations with Spark Matrix Factorization and ALS.mp4 58.3 MB
  • 03 Evaluating Recommender Systems/019 [Activity] Walkthrough of TestMetrics.py.mp4 57.0 MB
  • 11 Real-World Challenges of Recommender Systems/101 Temporal Effects and Value-Aware Recommendations.mp4 56.6 MB
  • 10 Scaling it Up/082 [Activity] Introduction and Installation of Apache Spark.mp4 55.9 MB
  • 05 Content-Based Filtering/027 [Activity] Producing and Evaluating Content-Based Movie Recommendations.mp4 54.9 MB
  • 06 Neighborhood-Based Collaborative Filtering/034 Item-based Collaborative Filtering.mp4 54.8 MB
  • 10 Scaling it Up/085 [Activity] Recommendations from 20 million ratings with Spark.mp4 53.1 MB
  • 08 Introduction to Deep Learning [Optional]/065 Intro to Recurrent Neural Networks (RNNs).mp4 52.1 MB
  • 09 Deep Learning for Recommender Systems/077 Clickstream Recommendations with RNNs.mp4 51.1 MB
  • 06 Neighborhood-Based Collaborative Filtering/033 [Activity] User-based Collaborative Filtering Hands-On.mp4 51.0 MB
  • 05 Content-Based Filtering/028 [Activity] Bleeding Edge Alert Mise en Scene Recommendations.mp4 48.8 MB
  • 02 Introduction to Python [Optional]/008 [Activity] The Basics of Python.mp4 45.1 MB
  • 09 Deep Learning for Recommender Systems/068 Intro to Deep Learning for Recommenders.mp4 44.7 MB
  • 10 Scaling it Up/086 Amazon DSSTNE.mp4 44.4 MB
  • 06 Neighborhood-Based Collaborative Filtering/041 [Exercise] Experiment with different KNN parameters..mp4 43.3 MB
  • 03 Evaluating Recommender Systems/013 Accuracy Metrics (RMSE MAE).mp4 42.2 MB
  • 04 A Recommender Engine Framework/023 [Activity] Recommender Engine Walkthrough Part 2.mp4 41.5 MB
  • 14 Wrapping Up/108 More to Explore.mp4 40.8 MB
  • 11 Real-World Challenges of Recommender Systems/099 Exercise Solution Outlier Removal.mp4 40.4 MB
  • 08 Introduction to Deep Learning [Optional]/053 Training Neural Networks.mp4 40.2 MB
  • 04 A Recommender Engine Framework/022 [Activity] Recommender Engine Walkthrough Part 1.mp4 39.7 MB
  • 09 Deep Learning for Recommender Systems/072 [Activity] Evaluating the RBM Recommender.mp4 39.5 MB
  • 07 Matrix Factorization Methods/045 [Activity] Running SVD and SVD on MovieLens.mp4 39.3 MB
  • 01 Getting Started/006 Top-N Recommender Architecture.mp4 38.9 MB
  • 08 Introduction to Deep Learning [Optional]/050 Deep Learning Pre-Requisites.mp4 38.8 MB
  • 04 A Recommender Engine Framework/024 [Activity] Review the Results of our Algorithm Evaluation..mp4 36.2 MB
  • 06 Neighborhood-Based Collaborative Filtering/032 User-based Collaborative Filtering.mp4 35.9 MB
  • 09 Deep Learning for Recommender Systems/073 [Exercise] Tuning Restricted Boltzmann Machines.mp4 35.2 MB
  • 13 Hybrid Approaches/107 Exercise Solution Hybrid Recommenders.mp4 34.8 MB
  • 04 A Recommender Engine Framework/021 Our Recommender Engine Architecture.mp4 34.3 MB
  • 09 Deep Learning for Recommender Systems/069 Restricted Boltzmann Machines (RBMs).mp4 33.2 MB
  • 08 Introduction to Deep Learning [Optional]/054 Tuning Neural Networks.mp4 32.8 MB
  • 06 Neighborhood-Based Collaborative Filtering/031 Similarity Metrics.mp4 32.2 MB
  • 03 Evaluating Recommender Systems/012 TrainTest and Cross Validation.mp4 30.5 MB
  • 11 Real-World Challenges of Recommender Systems/091 The Cold Start Problem (and solutions).mp4 29.1 MB
  • 09 Deep Learning for Recommender Systems/081 More Emerging Tech to Watch.mp4 29.0 MB
  • 01 Getting Started/003 Course Roadmap.mp4 28.9 MB
  • 12 Case Studies/104 Case Study Netflix Part 1.mp4 28.9 MB
  • 12 Case Studies/102 Case Study YouTube Part 1.mp4 28.2 MB
  • 09 Deep Learning for Recommender Systems/075 Auto-Encoders for Recommendations Deep Learning for Recs.mp4 28.2 MB
  • 01 Getting Started/004 Types of Recommenders.mp4 28.1 MB
  • 06 Neighborhood-Based Collaborative Filtering/035 [Activity] Item-based Collaborative Filtering Hands-On.mp4 28.1 MB
  • 11 Real-World Challenges of Recommender Systems/096 Exercise Solution Implement a Stoplist.mp4 28.0 MB
  • 12 Case Studies/105 Case Study Netflix Part 2.mp4 27.9 MB
  • 07 Matrix Factorization Methods/048 Bleeding Edge Alert Sparse Linear Methods (SLIM).mp4 27.7 MB
  • 12 Case Studies/103 Case Study YouTube Part 2.mp4 27.5 MB
  • 07 Matrix Factorization Methods/044 Singular Value Decomposition.mp4 26.3 MB
  • 06 Neighborhood-Based Collaborative Filtering/039 KNN Recommenders.mp4 26.1 MB
  • 08 Introduction to Deep Learning [Optional]/060 Classifier Patterns with Keras.mp4 26.0 MB
  • 03 Evaluating Recommender Systems/014 Top-N Hit Rate - Many Ways.mp4 25.7 MB
  • 02 Introduction to Python [Optional]/009 Data Structures in Python.mp4 25.6 MB
  • 11 Real-World Challenges of Recommender Systems/093 Exercise Solution Random Exploration.mp4 25.3 MB
  • 05 Content-Based Filtering/029 [Exercise] Dive Deeper into Content-Based Recommendations.mp4 25.3 MB
  • 06 Neighborhood-Based Collaborative Filtering/040 [Activity] Running User and Item-Based KNN on MovieLens.mp4 24.9 MB
  • 07 Matrix Factorization Methods/046 Improving on SVD.mp4 24.2 MB
  • 08 Introduction to Deep Learning [Optional]/063 CNN Architectures.mp4 23.6 MB
  • 03 Evaluating Recommender Systems/020 [Activity] Measure the Performance of SVD Recommendations.mp4 22.6 MB
  • 06 Neighborhood-Based Collaborative Filtering/042 Bleeding Edge Alert Translation-Based Recommendations.mp4 22.5 MB
  • 01 Getting Started/007 [Quiz] Review the basics of recommender systems..mp4 22.3 MB
  • 14 Wrapping Up/109 Bonus Lecture Companion Book and More Courses from Sundog Education.mp4 22.1 MB
  • 08 Introduction to Deep Learning [Optional]/066 Training Recurrent Neural Networks.mp4 21.7 MB
  • 01 Getting Started/005 Understanding You through Implicit and Explicit Ratings.mp4 21.7 MB
  • 11 Real-World Challenges of Recommender Systems/094 Stoplists.mp4 20.9 MB
  • 01 Getting Started/001 Udemy 101 Getting the Most From This Course.mp4 20.7 MB
  • 06 Neighborhood-Based Collaborative Filtering/036 [Exercise] Tuning Collaborative Filtering Algorithms.mp4 20.7 MB
  • 05 Content-Based Filtering/026 K-Nearest-Neighbors and Content Recs.mp4 20.6 MB
  • 13 Hybrid Approaches/106 Hybrid Recommenders and Exercise.mp4 19.3 MB
  • 08 Introduction to Deep Learning [Optional]/049 Deep Learning Introduction.mp4 18.5 MB
  • 10 Scaling it Up/083 Apache Spark Architecture.mp4 18.2 MB
  • 08 Introduction to Deep Learning [Optional]/058 Introduction to Keras.mp4 17.3 MB
  • 10 Scaling it Up/089 AWS SageMaker and Factorization Machines.mp4 16.3 MB
  • 06 Neighborhood-Based Collaborative Filtering/037 [Activity] Evaluating Collaborative Filtering Systems Offline.mp4 16.2 MB
  • 02 Introduction to Python [Optional]/011 [Exercise] Booleans loops and a hands-on challenge.mp4 14.5 MB
  • 03 Evaluating Recommender Systems/015 Coverage Diversity and Novelty.mp4 14.4 MB
  • 03 Evaluating Recommender Systems/017 [Quiz] Review ways to measure your recommender..mp4 13.5 MB
  • 07 Matrix Factorization Methods/047 [Exercise] Tune the hyperparameters on SVD.mp4 13.1 MB
  • 02 Introduction to Python [Optional]/010 Functions in Python.mp4 12.9 MB
  • 09 Deep Learning for Recommender Systems/074 Exercise Results Tuning a RBM Recommender.mp4 12.4 MB
  • 10 Scaling it Up/088 Scaling Up DSSTNE.mp4 10.9 MB
  • 06 Neighborhood-Based Collaborative Filtering/038 [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering.mp4 10.0 MB
  • 09 Deep Learning for Recommender Systems/078 [Exercise] Get GRU4Rec Working on your Desktop.mp4 7.8 MB
  • 11 Real-World Challenges of Recommender Systems/092 [Exercise] Implement Random Exploration.mp4 2.3 MB
  • 11 Real-World Challenges of Recommender Systems/098 [Exercise] Identify and Eliminate Outlier Users.mp4 1.9 MB
  • 11 Real-World Challenges of Recommender Systems/095 [Exercise] Implement a Stoplist.mp4 1.4 MB
  • 08 Introduction to Deep Learning [Optional]/056 [Activity] Handwriting Recognition with Tensorflow part 1-en.srt 34.1 kB
  • 08 Introduction to Deep Learning [Optional]/055 Introduction to Tensorflow-en.srt 26.6 kB
  • 09 Deep Learning for Recommender Systems/070 [Activity] Recommendations with RBMs part 1-en.srt 25.7 kB
  • 08 Introduction to Deep Learning [Optional]/052 [Activity] Playing with Tensorflow-en.srt 23.8 kB
  • 08 Introduction to Deep Learning [Optional]/067 [Activity] Sentiment Analysis of Movie Reviews using RNNs and Keras-en.srt 23.2 kB
  • 08 Introduction to Deep Learning [Optional]/051 History of Artificial Neural Networks-en.srt 23.0 kB
  • 08 Introduction to Deep Learning [Optional]/059 [Activity] Handwriting Recognition with Keras-en.srt 20.1 kB
  • 06 Neighborhood-Based Collaborative Filtering/031 Similarity Metrics-en.srt 19.3 kB
  • 08 Introduction to Deep Learning [Optional]/062 Intro to Convolutional Neural Networks (CNNs)-en.srt 18.4 kB
  • 08 Introduction to Deep Learning [Optional]/061 [Exercise] Predict Political Parties of Politicians with Keras-en.srt 18.3 kB
  • 08 Introduction to Deep Learning [Optional]/050 Deep Learning Pre-Requisites-en.srt 18.3 kB
  • 05 Content-Based Filtering/025 Content-Based Recommendations and the Cosine Similarity Metric-en.srt 18.3 kB
  • 08 Introduction to Deep Learning [Optional]/064 [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs)-en.srt 17.2 kB
  • 09 Deep Learning for Recommender Systems/079 Exercise Results GRU4Rec in Action-en.srt 16.7 kB
  • 09 Deep Learning for Recommender Systems/069 Restricted Boltzmann Machines (RBMs)-en.srt 16.6 kB
  • 08 Introduction to Deep Learning [Optional]/065 Intro to Recurrent Neural Networks (RNNs)-en.srt 15.9 kB
  • 01 Getting Started/002 [Activity] Install Anaconda course materials and create movie recommendations-en.srt 15.6 kB
  • 09 Deep Learning for Recommender Systems/077 Clickstream Recommendations with RNNs-en.srt 15.5 kB
  • 04 A Recommender Engine Framework/021 Our Recommender Engine Architecture-en.srt 15.2 kB
  • 06 Neighborhood-Based Collaborative Filtering/032 User-based Collaborative Filtering-en.srt 14.6 kB
  • 12 Case Studies/103 Case Study YouTube Part 2-en.srt 14.6 kB
  • 09 Deep Learning for Recommender Systems/071 [Activity] Recommendations with RBMs part 2-en.srt 14.4 kB
  • 07 Matrix Factorization Methods/043 Principal Component Analysis (PCA)-en.srt 14.4 kB
  • 07 Matrix Factorization Methods/044 Singular Value Decomposition-en.srt 13.9 kB
  • 08 Introduction to Deep Learning [Optional]/057 [Activity] Handwriting Recognition with Tensorflow part 2-en.srt 13.7 kB
  • 09 Deep Learning for Recommender Systems/076 [Activity] Recommendations with Deep Neural Networks-en.srt 13.7 kB
  • 11 Real-World Challenges of Recommender Systems/091 The Cold Start Problem (and solutions)-en.srt 13.6 kB
  • 10 Scaling it Up/090 SageMaker in Action Factorization Machines on one million ratings in the cloud-en.srt 13.2 kB
  • 03 Evaluating Recommender Systems/018 [Activity] Walkthrough of RecommenderMetrics.py-en.srt 13.1 kB
  • 08 Introduction to Deep Learning [Optional]/053 Training Neural Networks-en.srt 12.8 kB
  • 11 Real-World Challenges of Recommender Systems/097 Filter Bubbles Trust and Outliers-en.srt 12.4 kB
  • 10 Scaling it Up/087 DSSTNE in Action-en.srt 11.9 kB
  • 01 Getting Started/006 Top-N Recommender Architecture-en.srt 11.9 kB
  • 09 Deep Learning for Recommender Systems/080 Bleeding Edge Alert Deep Factorization Machines-en.srt 11.7 kB
  • 10 Scaling it Up/084 [Activity] Movie Recommendations with Spark Matrix Factorization and ALS-en.srt 11.4 kB
  • 03 Evaluating Recommender Systems/016 Churn Responsiveness and AB Tests-en.srt 11.2 kB
  • 06 Neighborhood-Based Collaborative Filtering/030 Measuring Similarity and Sparsity-en.srt 11.0 kB
  • 03 Evaluating Recommender Systems/019 [Activity] Walkthrough of TestMetrics.py-en.srt 10.9 kB
  • 09 Deep Learning for Recommender Systems/081 More Emerging Tech to Watch-en.srt 10.8 kB
  • 03 Evaluating Recommender Systems/015 Coverage Diversity and Novelty-en.srt 10.8 kB
  • 05 Content-Based Filtering/027 [Activity] Producing and Evaluating Content-Based Movie Recommendations-en.srt 10.6 kB
  • 11 Real-World Challenges of Recommender Systems/094 Stoplists-en.srt 10.5 kB
  • 10 Scaling it Up/083 Apache Spark Architecture-en.srt 10.5 kB
  • 11 Real-World Challenges of Recommender Systems/100 Fraud The Perils of Clickstream and International Concerns-en.srt 9.9 kB
  • 06 Neighborhood-Based Collaborative Filtering/033 [Activity] User-based Collaborative Filtering Hands-On-en.srt 9.8 kB
  • 02 Introduction to Python [Optional]/009 Data Structures in Python-en.srt 9.7 kB
  • 09 Deep Learning for Recommender Systems/075 Auto-Encoders for Recommendations Deep Learning for Recs-en.srt 9.6 kB
  • 10 Scaling it Up/086 Amazon DSSTNE-en.srt 9.4 kB
  • 03 Evaluating Recommender Systems/014 Top-N Hit Rate - Many Ways-en.srt 9.4 kB
  • 07 Matrix Factorization Methods/046 Improving on SVD-en.srt 9.3 kB
  • 01 Getting Started/007 [Quiz] Review the basics of recommender systems.-en.srt 9.1 kB
  • 06 Neighborhood-Based Collaborative Filtering/034 Item-based Collaborative Filtering-en.srt 9.0 kB
  • 06 Neighborhood-Based Collaborative Filtering/041 [Exercise] Experiment with different KNN parameters.-en.srt 9.0 kB
  • 01 Getting Started/005 Understanding You through Implicit and Explicit Ratings-en.srt 9.0 kB
  • 02 Introduction to Python [Optional]/008 [Activity] The Basics of Python-en.srt 8.9 kB
  • 05 Content-Based Filtering/028 [Activity] Bleeding Edge Alert Mise en Scene Recommendations-en.srt 8.8 kB
  • 05 Content-Based Filtering/029 [Exercise] Dive Deeper into Content-Based Recommendations-en.srt 8.7 kB
  • 03 Evaluating Recommender Systems/013 Accuracy Metrics (RMSE MAE)-en.srt 8.7 kB
  • 10 Scaling it Up/089 AWS SageMaker and Factorization Machines-en.srt 8.6 kB
  • 10 Scaling it Up/085 [Activity] Recommendations from 20 million ratings with Spark-en.srt 8.6 kB
  • 13 Hybrid Approaches/107 Exercise Solution Hybrid Recommenders-en.srt 8.5 kB
  • 03 Evaluating Recommender Systems/012 TrainTest and Cross Validation-en.srt 8.5 kB
  • 01 Getting Started/003 Course Roadmap-en.srt 8.5 kB
  • 08 Introduction to Deep Learning [Optional]/054 Tuning Neural Networks-en.srt 8.5 kB
  • 06 Neighborhood-Based Collaborative Filtering/039 KNN Recommenders-en.srt 8.4 kB
  • 10 Scaling it Up/082 [Activity] Introduction and Installation of Apache Spark-en.srt 8.4 kB
  • 05 Content-Based Filtering/026 K-Nearest-Neighbors and Content Recs-en.srt 8.3 kB
  • 08 Introduction to Deep Learning [Optional]/060 Classifier Patterns with Keras-en.srt 8.2 kB
  • 04 A Recommender Engine Framework/023 [Activity] Recommender Engine Walkthrough Part 2-en.srt 8.1 kB
  • 12 Case Studies/104 Case Study Netflix Part 1-en.srt 7.8 kB
  • 12 Case Studies/105 Case Study Netflix Part 2-en.srt 7.8 kB
  • 07 Matrix Factorization Methods/048 Bleeding Edge Alert Sparse Linear Methods (SLIM)-en.srt 7.7 kB
  • 11 Real-World Challenges of Recommender Systems/099 Exercise Solution Outlier Removal-en.srt 7.7 kB
  • 11 Real-World Challenges of Recommender Systems/101 Temporal Effects and Value-Aware Recommendations-en.srt 7.7 kB
  • 04 A Recommender Engine Framework/022 [Activity] Recommender Engine Walkthrough Part 1-en.srt 7.6 kB
  • 12 Case Studies/102 Case Study YouTube Part 1-en.srt 7.4 kB
  • 06 Neighborhood-Based Collaborative Filtering/036 [Exercise] Tuning Collaborative Filtering Algorithms-en.srt 7.1 kB
  • 01 Getting Started/004 Types of Recommenders-en.srt 6.8 kB
  • 09 Deep Learning for Recommender Systems/072 [Activity] Evaluating the RBM Recommender-en.srt 6.8 kB
  • 08 Introduction to Deep Learning [Optional]/066 Training Recurrent Neural Networks-en.srt 6.8 kB
  • 07 Matrix Factorization Methods/045 [Activity] Running SVD and SVD on MovieLens-en.srt 6.5 kB
  • 04 A Recommender Engine Framework/024 [Activity] Review the Results of our Algorithm Evaluation.-en.srt 6.5 kB
  • 02 Introduction to Python [Optional]/011 [Exercise] Booleans loops and a hands-on challenge-en.srt 6.4 kB
  • 08 Introduction to Deep Learning [Optional]/063 CNN Architectures-en.srt 6.4 kB
  • 08 Introduction to Deep Learning [Optional]/058 Introduction to Keras-en.srt 6.3 kB
  • 13 Hybrid Approaches/106 Hybrid Recommenders and Exercise-en.srt 5.6 kB
  • 03 Evaluating Recommender Systems/017 [Quiz] Review ways to measure your recommender.-en.srt 5.6 kB
  • 09 Deep Learning for Recommender Systems/078 [Exercise] Get GRU4Rec Working on your Desktop-en.srt 5.4 kB
  • 02 Introduction to Python [Optional]/010 Functions in Python-en.srt 5.3 kB
  • 03 Evaluating Recommender Systems/020 [Activity] Measure the Performance of SVD Recommendations-en.srt 5.1 kB
  • 14 Wrapping Up/108 More to Explore-en.srt 5.1 kB
  • 06 Neighborhood-Based Collaborative Filtering/042 Bleeding Edge Alert Translation-Based Recommendations-en.srt 5.0 kB
  • 06 Neighborhood-Based Collaborative Filtering/035 [Activity] Item-based Collaborative Filtering Hands-On-en.srt 5.0 kB
  • 09 Deep Learning for Recommender Systems/068 Intro to Deep Learning for Recommenders-en.srt 4.9 kB
  • 06 Neighborhood-Based Collaborative Filtering/040 [Activity] Running User and Item-Based KNN on MovieLens-en.srt 4.8 kB
  • 06 Neighborhood-Based Collaborative Filtering/038 [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering-en.srt 4.6 kB
  • 11 Real-World Challenges of Recommender Systems/096 Exercise Solution Implement a Stoplist-en.srt 4.4 kB
  • 10 Scaling it Up/088 Scaling Up DSSTNE-en.srt 4.3 kB
  • 11 Real-World Challenges of Recommender Systems/093 Exercise Solution Random Exploration-en.srt 4.3 kB
  • 07 Matrix Factorization Methods/047 [Exercise] Tune the hyperparameters on SVD-en.srt 4.1 kB
  • 01 Getting Started/001 Udemy 101 Getting the Most From This Course-en.srt 4.0 kB
  • 09 Deep Learning for Recommender Systems/073 [Exercise] Tuning Restricted Boltzmann Machines-en.srt 3.9 kB
  • 08 Introduction to Deep Learning [Optional]/049 Deep Learning Introduction-en.srt 3.4 kB
  • 06 Neighborhood-Based Collaborative Filtering/037 [Activity] Evaluating Collaborative Filtering Systems Offline-en.srt 2.6 kB
  • 09 Deep Learning for Recommender Systems/074 Exercise Results Tuning a RBM Recommender-en.srt 2.5 kB
  • 11 Real-World Challenges of Recommender Systems/092 [Exercise] Implement Random Exploration-en.srt 1.8 kB
  • 14 Wrapping Up/109 Bonus Lecture Companion Book and More Courses from Sundog Education-en.srt 1.7 kB
  • 11 Real-World Challenges of Recommender Systems/098 [Exercise] Identify and Eliminate Outlier Users-en.srt 1.7 kB
  • 11 Real-World Challenges of Recommender Systems/095 [Exercise] Implement a Stoplist-en.srt 1.2 kB
  • udemycoursedownloader.com.url 132 Bytes
  • Udemy Course downloader.txt 94 Bytes
  • 14 Wrapping Up/109 Sundog-Education-website.txt 35 Bytes
  • 14 Wrapping Up/109 Building-Recommender-Systems-book-on-Amazon.txt 23 Bytes

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