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种子名称： [CourseClub.NET] Packtpub - Building Recommender Systems with Machine Learning and AI
文件类型： 视频
文件数目： 107个文件
文件大小： 2.89 GB
收录时间： 2020-1-16 15:09
已经下载： 3次
资源热度： 275
最近下载： 2024-12-23 16:20

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[CourseClub.NET] Packtpub - Building Recommender Systems with Machine Learning and AI.torrent

01.Getting Started/0101.Install Anaconda, course materials, and create movie recommendations!.mp488.13MB

01.Getting Started/0102.Course Roadmap.mp469.27MB

01.Getting Started/0103.Types of Recommenders.mp414.11MB

01.Getting Started/0104.Understanding You through Implicit and Explicit Ratings.mp49.2MB

01.Getting Started/0105.Top-N Recommender Architecture.mp415.32MB

01.Getting Started/0106.Review the basics of recommender systems..mp411.16MB

02.Introduction to Python/0201.The Basics of Python.mp442MB

02.Introduction to Python/0202.Data Structures in Python.mp411.59MB

02.Introduction to Python/0203.Functions in Python.mp45.85MB

02.Introduction to Python/0204.Booleans, loops, and a hands-on challenge.mp47.33MB

03.Evaluating Recommender Systems/0301.TrainTest and Cross Validation.mp423.17MB

03.Evaluating Recommender Systems/0302.Accuracy Metrics (RMSE, MAE).mp446.73MB

03.Evaluating Recommender Systems/0303.Top-N Hit Rate - Many Ways.mp412.16MB

03.Evaluating Recommender Systems/0304.Coverage, Diversity, and Novelty.mp47.94MB

03.Evaluating Recommender Systems/0305.Churn, Responsiveness, and AB Tests.mp482.68MB

03.Evaluating Recommender Systems/0306.Review ways to measure your recommender..mp48.26MB

03.Evaluating Recommender Systems/0307.Walkthrough of RecommenderMetrics.py.mp438.78MB

03.Evaluating Recommender Systems/0308.Walkthrough of TestMetrics.py.mp425.34MB

03.Evaluating Recommender Systems/0309.Measure the Performance of SVD Recommendations.mp412.05MB

04.A Recommender Engine Framework/0401.Our Recommender Engine Architecture.mp418.17MB

04.A Recommender Engine Framework/0402.Recommender Engine Walkthrough, Part 1.mp418.55MB

04.A Recommender Engine Framework/0403.Recommender Engine Walkthrough, Part 2.mp418.57MB

04.A Recommender Engine Framework/0404.Review the Results of our Algorithm Evaluation..mp414.3MB

05.Content-Based Filtering/0501.Content-Based Recommendations, and the Cosine Similarity Metric.mp438.47MB

05.Content-Based Filtering/0502.K-Nearest-Neighbors and Content Recs.mp411.84MB

05.Content-Based Filtering/0503.Producing and Evaluating Content-Based Movie Recommendations.mp427.89MB

05.Content-Based Filtering/0504.Bleeding Edge Alert! Mise en Scene Recommendations.mp433.71MB

05.Content-Based Filtering/0505.Dive Deeper into Content-Based Recommendations.mp410.66MB

06.Neighborhood-Based Collaborative Filtering/0601.Measuring Similarity, and Sparsity.mp469.75MB

06.Neighborhood-Based Collaborative Filtering/0602.Similarity Metrics.mp415.45MB

06.Neighborhood-Based Collaborative Filtering/0603.User-based Collaborative Filtering.mp419.99MB

06.Neighborhood-Based Collaborative Filtering/0604.User-based Collaborative Filtering, Hands-On.mp424.56MB

06.Neighborhood-Based Collaborative Filtering/0605.Item-based Collaborative Filtering.mp461.59MB

06.Neighborhood-Based Collaborative Filtering/0606.Item-based Collaborative Filtering, Hands-On.mp418.12MB

06.Neighborhood-Based Collaborative Filtering/0607.Tuning Collaborative Filtering Algorithms.mp410.06MB

06.Neighborhood-Based Collaborative Filtering/0608.Evaluating Collaborative Filtering Systems Offline.mp410.57MB

06.Neighborhood-Based Collaborative Filtering/0609.Measure the Hit Rate of Item-Based Collaborative Filtering.mp44.43MB

06.Neighborhood-Based Collaborative Filtering/0610.KNN Recommenders.mp421.88MB

06.Neighborhood-Based Collaborative Filtering/0611.Running User and Item-Based KNN on MovieLens.mp419.63MB

06.Neighborhood-Based Collaborative Filtering/0612.Experiment with different KNN parameters..mp438.78MB

06.Neighborhood-Based Collaborative Filtering/0613.Bleeding Edge Alert! Translation-Based Recommendations.mp419.64MB

07.Matrix Factorization Methods/0701.Principal Component Analysis (PCA).mp464.98MB

07.Matrix Factorization Methods/0702.Singular Value Decomposition.mp412.98MB

07.Matrix Factorization Methods/0703.Running SVD and SVD++ on MovieLens.mp423.12MB

07.Matrix Factorization Methods/0704.Improving on SVD.mp49.69MB

07.Matrix Factorization Methods/0705.Tune the hyperparameters on SVD.mp48.02MB

07.Matrix Factorization Methods/0706.Bleeding Edge Alert! Sparse Linear Methods (SLIM).mp421.08MB

08.Introduction to Deep Learning/0801.Deep Learning Introduction.mp422.8MB

08.Introduction to Deep Learning/0802.Deep Learning Pre-Requisites.mp420.12MB

08.Introduction to Deep Learning/0803.History of Artificial Neural Networks.mp440.44MB

08.Introduction to Deep Learning/0804.[Activity] Playing with Tensorflow.mp4116.91MB

08.Introduction to Deep Learning/0805.Training Neural Networks.mp418.84MB

08.Introduction to Deep Learning/0806.Tuning Neural Networks.mp413.11MB

08.Introduction to Deep Learning/0807.Introduction to Tensorflow.mp443MB

08.Introduction to Deep Learning/0808.[Activity] Handwriting Recognition with Tensorflow, part 1.mp492.89MB

08.Introduction to Deep Learning/0809.[Activity] Handwriting Recognition with Tensorflow, part 2.mp427.4MB

08.Introduction to Deep Learning/0810.Introduction to Keras.mp46.67MB

08.Introduction to Deep Learning/0811.[Activity] Handwriting Recognition with Keras.mp446.94MB

08.Introduction to Deep Learning/0812.Classifier Patterns with Keras.mp413.12MB

08.Introduction to Deep Learning/0813.[Exercise] Predict Political Parties of Politicians with Keras.mp453.7MB

08.Introduction to Deep Learning/0814.Intro to Convolutional Neural Networks (CNN_s).mp436.4MB

08.Introduction to Deep Learning/0815.CNN Architectures.mp49.65MB

08.Introduction to Deep Learning/0816.[Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp442.41MB

08.Introduction to Deep Learning/0817.Intro to Recurrent Neural Networks (RNN_s).mp422.49MB

08.Introduction to Deep Learning/0818.Training Recurrent Neural Networks.mp410.1MB

08.Introduction to Deep Learning/0819.[Activity] Sentiment Analysis of Movie Reviews using RNN_s and Keras.mp473.37MB

09.Deep Learning for Recommender Systems/0901.Intro to Deep Learning for Recommenders.mp455.99MB

09.Deep Learning for Recommender Systems/0902.Restricted Boltzmann Machines (RBM_s).mp415.93MB

09.Deep Learning for Recommender Systems/0903.[Activity] Recommendations with RBM_s, part 1.mp450.52MB

09.Deep Learning for Recommender Systems/0904.[Activity] Recommendations with RBM_s, part 2.mp426.41MB

09.Deep Learning for Recommender Systems/0905.[Activity] Evaluating the RBM Recommender.mp419.85MB

09.Deep Learning for Recommender Systems/0906.[Exercise] Tuning Restricted Boltzmann Machines.mp453.71MB

09.Deep Learning for Recommender Systems/0907.Exercise Results Tuning a RBM Recommender.mp46.63MB

09.Deep Learning for Recommender Systems/0908.Auto-Encoders for Recommendations Deep Learning for Recs.mp411.82MB

09.Deep Learning for Recommender Systems/0909.[Activity] Recommendations with Deep Neural Networks.mp437.22MB

09.Deep Learning for Recommender Systems/0910.Clickstream Recommendations with RNN_s.mp424.84MB

09.Deep Learning for Recommender Systems/0911.[Exercise] Get GRU4Rec Working on your Desktop.mp43.88MB

09.Deep Learning for Recommender Systems/0912.Exercise Results GRU4Rec in Action.mp441.06MB

09.Deep Learning for Recommender Systems/0913.Bleeding Edge Alert! Deep Factorization Machines.mp444.31MB

09.Deep Learning for Recommender Systems/0914.More Emerging Tech to Watch.mp414.16MB

10.Scaling it up/1001.[Activity] Introduction and Installation of Apache Spark.mp440.04MB

10.Scaling it up/1002.Apache Spark Architecture.mp49.37MB

10.Scaling it up/1003.[Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.mp423.76MB

10.Scaling it up/1004.[Activity] Recommendations from 20 million ratings with Spark.mp426.92MB

10.Scaling it up/1005.Amazon DSSTNE.mp441.35MB

10.Scaling it up/1006.DSSTNE in Action.mp461.12MB

10.Scaling it up/1007.Scaling Up DSSTNE.mp44.82MB

10.Scaling it up/1008.AWS SageMaker and Factorization Machines.mp47.95MB

10.Scaling it up/1009.SageMaker in Action Factorization Machines on one million ratings, in the cloud.mp444.2MB

11.11 Real-World Challenges of Recommender Systems/1101.The Cold Start Problem (and solutions).mp411.8MB

11.11 Real-World Challenges of Recommender Systems/1102.[Exercise] Implement Random Exploration.mp41.19MB

11.11 Real-World Challenges of Recommender Systems/1103.Exercise Solution Random Exploration.mp415.43MB

11.11 Real-World Challenges of Recommender Systems/1104.Stoplists.mp48.67MB

11.11 Real-World Challenges of Recommender Systems/1105.[Exercise] Implement a Stoplist.mp4761.82KB

11.11 Real-World Challenges of Recommender Systems/1106.Exercise Solution Implement a Stoplist.mp415.07MB

11.11 Real-World Challenges of Recommender Systems/1107.Filter Bubbles, Trust, and Outliers.mp421.76MB

11.11 Real-World Challenges of Recommender Systems/1108.[Exercise] Identify and Eliminate Outlier Users.mp41020.31KB

11.11 Real-World Challenges of Recommender Systems/1109.Exercise Solution Outlier Removal.mp416.61MB

11.11 Real-World Challenges of Recommender Systems/1110.Fraud, the Perils of Clickstream, and International Concerns.mp472.79MB

11.11 Real-World Challenges of Recommender Systems/1111.Temporal Effects, and Value-Aware Recommendations.mp481.63MB

12.Case Studies/1201.Case Study YouTube, Part 1.mp412.79MB

12.Case Studies/1202.Case Study YouTube, Part 2.mp412.47MB

12.Case Studies/1203.Case Study Netflix, Part 1.mp413.85MB

12.Case Studies/1204.Case Study Netflix, Part 2.mp49.84MB

13.Hybrid Approaches/1301.Hybrid Recommenders and Exercise.mp48.82MB

13.Hybrid Approaches/1302.Exercise Solution Hybrid Recommenders.mp420.42MB

14.Wrapping Up/1401.More to Explore.mp461.91MB