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[CourseClub.NET] Packtpub - Building Recommender Systems with Machine Learning and AI
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2020-1-16 15:09
2024-8-14 08:46
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Building
Recommender
Systems
with
Machine
Learning
and
AI
文件列表
01.Getting Started/0101.Install Anaconda, course materials, and create movie recommendations!.mp4
88.13MB
01.Getting Started/0102.Course Roadmap.mp4
69.27MB
01.Getting Started/0103.Types of Recommenders.mp4
14.11MB
01.Getting Started/0104.Understanding You through Implicit and Explicit Ratings.mp4
9.2MB
01.Getting Started/0105.Top-N Recommender Architecture.mp4
15.32MB
01.Getting Started/0106.Review the basics of recommender systems..mp4
11.16MB
02.Introduction to Python/0201.The Basics of Python.mp4
42MB
02.Introduction to Python/0202.Data Structures in Python.mp4
11.59MB
02.Introduction to Python/0203.Functions in Python.mp4
5.85MB
02.Introduction to Python/0204.Booleans, loops, and a hands-on challenge.mp4
7.33MB
03.Evaluating Recommender Systems/0301.TrainTest and Cross Validation.mp4
23.17MB
03.Evaluating Recommender Systems/0302.Accuracy Metrics (RMSE, MAE).mp4
46.73MB
03.Evaluating Recommender Systems/0303.Top-N Hit Rate - Many Ways.mp4
12.16MB
03.Evaluating Recommender Systems/0304.Coverage, Diversity, and Novelty.mp4
7.94MB
03.Evaluating Recommender Systems/0305.Churn, Responsiveness, and AB Tests.mp4
82.68MB
03.Evaluating Recommender Systems/0306.Review ways to measure your recommender..mp4
8.26MB
03.Evaluating Recommender Systems/0307.Walkthrough of RecommenderMetrics.py.mp4
38.78MB
03.Evaluating Recommender Systems/0308.Walkthrough of TestMetrics.py.mp4
25.34MB
03.Evaluating Recommender Systems/0309.Measure the Performance of SVD Recommendations.mp4
12.05MB
04.A Recommender Engine Framework/0401.Our Recommender Engine Architecture.mp4
18.17MB
04.A Recommender Engine Framework/0402.Recommender Engine Walkthrough, Part 1.mp4
18.55MB
04.A Recommender Engine Framework/0403.Recommender Engine Walkthrough, Part 2.mp4
18.57MB
04.A Recommender Engine Framework/0404.Review the Results of our Algorithm Evaluation..mp4
14.3MB
05.Content-Based Filtering/0501.Content-Based Recommendations, and the Cosine Similarity Metric.mp4
38.47MB
05.Content-Based Filtering/0502.K-Nearest-Neighbors and Content Recs.mp4
11.84MB
05.Content-Based Filtering/0503.Producing and Evaluating Content-Based Movie Recommendations.mp4
27.89MB
05.Content-Based Filtering/0504.Bleeding Edge Alert! Mise en Scene Recommendations.mp4
33.71MB
05.Content-Based Filtering/0505.Dive Deeper into Content-Based Recommendations.mp4
10.66MB
06.Neighborhood-Based Collaborative Filtering/0601.Measuring Similarity, and Sparsity.mp4
69.75MB
06.Neighborhood-Based Collaborative Filtering/0602.Similarity Metrics.mp4
15.45MB
06.Neighborhood-Based Collaborative Filtering/0603.User-based Collaborative Filtering.mp4
19.99MB
06.Neighborhood-Based Collaborative Filtering/0604.User-based Collaborative Filtering, Hands-On.mp4
24.56MB
06.Neighborhood-Based Collaborative Filtering/0605.Item-based Collaborative Filtering.mp4
61.59MB
06.Neighborhood-Based Collaborative Filtering/0606.Item-based Collaborative Filtering, Hands-On.mp4
18.12MB
06.Neighborhood-Based Collaborative Filtering/0607.Tuning Collaborative Filtering Algorithms.mp4
10.06MB
06.Neighborhood-Based Collaborative Filtering/0608.Evaluating Collaborative Filtering Systems Offline.mp4
10.57MB
06.Neighborhood-Based Collaborative Filtering/0609.Measure the Hit Rate of Item-Based Collaborative Filtering.mp4
4.43MB
06.Neighborhood-Based Collaborative Filtering/0610.KNN Recommenders.mp4
21.88MB
06.Neighborhood-Based Collaborative Filtering/0611.Running User and Item-Based KNN on MovieLens.mp4
19.63MB
06.Neighborhood-Based Collaborative Filtering/0612.Experiment with different KNN parameters..mp4
38.78MB
06.Neighborhood-Based Collaborative Filtering/0613.Bleeding Edge Alert! Translation-Based Recommendations.mp4
19.64MB
07.Matrix Factorization Methods/0701.Principal Component Analysis (PCA).mp4
64.98MB
07.Matrix Factorization Methods/0702.Singular Value Decomposition.mp4
12.98MB
07.Matrix Factorization Methods/0703.Running SVD and SVD++ on MovieLens.mp4
23.12MB
07.Matrix Factorization Methods/0704.Improving on SVD.mp4
9.69MB
07.Matrix Factorization Methods/0705.Tune the hyperparameters on SVD.mp4
8.02MB
07.Matrix Factorization Methods/0706.Bleeding Edge Alert! Sparse Linear Methods (SLIM).mp4
21.08MB
08.Introduction to Deep Learning/0801.Deep Learning Introduction.mp4
22.8MB
08.Introduction to Deep Learning/0802.Deep Learning Pre-Requisites.mp4
20.12MB
08.Introduction to Deep Learning/0803.History of Artificial Neural Networks.mp4
40.44MB
08.Introduction to Deep Learning/0804.[Activity] Playing with Tensorflow.mp4
116.91MB
08.Introduction to Deep Learning/0805.Training Neural Networks.mp4
18.84MB
08.Introduction to Deep Learning/0806.Tuning Neural Networks.mp4
13.11MB
08.Introduction to Deep Learning/0807.Introduction to Tensorflow.mp4
43MB
08.Introduction to Deep Learning/0808.[Activity] Handwriting Recognition with Tensorflow, part 1.mp4
92.89MB
08.Introduction to Deep Learning/0809.[Activity] Handwriting Recognition with Tensorflow, part 2.mp4
27.4MB
08.Introduction to Deep Learning/0810.Introduction to Keras.mp4
6.67MB
08.Introduction to Deep Learning/0811.[Activity] Handwriting Recognition with Keras.mp4
46.94MB
08.Introduction to Deep Learning/0812.Classifier Patterns with Keras.mp4
13.12MB
08.Introduction to Deep Learning/0813.[Exercise] Predict Political Parties of Politicians with Keras.mp4
53.7MB
08.Introduction to Deep Learning/0814.Intro to Convolutional Neural Networks (CNN_s).mp4
36.4MB
08.Introduction to Deep Learning/0815.CNN Architectures.mp4
9.65MB
08.Introduction to Deep Learning/0816.[Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp4
42.41MB
08.Introduction to Deep Learning/0817.Intro to Recurrent Neural Networks (RNN_s).mp4
22.49MB
08.Introduction to Deep Learning/0818.Training Recurrent Neural Networks.mp4
10.1MB
08.Introduction to Deep Learning/0819.[Activity] Sentiment Analysis of Movie Reviews using RNN_s and Keras.mp4
73.37MB
09.Deep Learning for Recommender Systems/0901.Intro to Deep Learning for Recommenders.mp4
55.99MB
09.Deep Learning for Recommender Systems/0902.Restricted Boltzmann Machines (RBM_s).mp4
15.93MB
09.Deep Learning for Recommender Systems/0903.[Activity] Recommendations with RBM_s, part 1.mp4
50.52MB
09.Deep Learning for Recommender Systems/0904.[Activity] Recommendations with RBM_s, part 2.mp4
26.41MB
09.Deep Learning for Recommender Systems/0905.[Activity] Evaluating the RBM Recommender.mp4
19.85MB
09.Deep Learning for Recommender Systems/0906.[Exercise] Tuning Restricted Boltzmann Machines.mp4
53.71MB
09.Deep Learning for Recommender Systems/0907.Exercise Results Tuning a RBM Recommender.mp4
6.63MB
09.Deep Learning for Recommender Systems/0908.Auto-Encoders for Recommendations Deep Learning for Recs.mp4
11.82MB
09.Deep Learning for Recommender Systems/0909.[Activity] Recommendations with Deep Neural Networks.mp4
37.22MB
09.Deep Learning for Recommender Systems/0910.Clickstream Recommendations with RNN_s.mp4
24.84MB
09.Deep Learning for Recommender Systems/0911.[Exercise] Get GRU4Rec Working on your Desktop.mp4
3.88MB
09.Deep Learning for Recommender Systems/0912.Exercise Results GRU4Rec in Action.mp4
41.06MB
09.Deep Learning for Recommender Systems/0913.Bleeding Edge Alert! Deep Factorization Machines.mp4
44.31MB
09.Deep Learning for Recommender Systems/0914.More Emerging Tech to Watch.mp4
14.16MB
10.Scaling it up/1001.[Activity] Introduction and Installation of Apache Spark.mp4
40.04MB
10.Scaling it up/1002.Apache Spark Architecture.mp4
9.37MB
10.Scaling it up/1003.[Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.mp4
23.76MB
10.Scaling it up/1004.[Activity] Recommendations from 20 million ratings with Spark.mp4
26.92MB
10.Scaling it up/1005.Amazon DSSTNE.mp4
41.35MB
10.Scaling it up/1006.DSSTNE in Action.mp4
61.12MB
10.Scaling it up/1007.Scaling Up DSSTNE.mp4
4.82MB
10.Scaling it up/1008.AWS SageMaker and Factorization Machines.mp4
7.95MB
10.Scaling it up/1009.SageMaker in Action Factorization Machines on one million ratings, in the cloud.mp4
44.2MB
11.11 Real-World Challenges of Recommender Systems/1101.The Cold Start Problem (and solutions).mp4
11.8MB
11.11 Real-World Challenges of Recommender Systems/1102.[Exercise] Implement Random Exploration.mp4
1.19MB
11.11 Real-World Challenges of Recommender Systems/1103.Exercise Solution Random Exploration.mp4
15.43MB
11.11 Real-World Challenges of Recommender Systems/1104.Stoplists.mp4
8.67MB
11.11 Real-World Challenges of Recommender Systems/1105.[Exercise] Implement a Stoplist.mp4
761.82KB
11.11 Real-World Challenges of Recommender Systems/1106.Exercise Solution Implement a Stoplist.mp4
15.07MB
11.11 Real-World Challenges of Recommender Systems/1107.Filter Bubbles, Trust, and Outliers.mp4
21.76MB
11.11 Real-World Challenges of Recommender Systems/1108.[Exercise] Identify and Eliminate Outlier Users.mp4
1020.31KB
11.11 Real-World Challenges of Recommender Systems/1109.Exercise Solution Outlier Removal.mp4
16.61MB
11.11 Real-World Challenges of Recommender Systems/1110.Fraud, the Perils of Clickstream, and International Concerns.mp4
72.79MB
11.11 Real-World Challenges of Recommender Systems/1111.Temporal Effects, and Value-Aware Recommendations.mp4
81.63MB
12.Case Studies/1201.Case Study YouTube, Part 1.mp4
12.79MB
12.Case Studies/1202.Case Study YouTube, Part 2.mp4
12.47MB
12.Case Studies/1203.Case Study Netflix, Part 1.mp4
13.85MB
12.Case Studies/1204.Case Study Netflix, Part 2.mp4
9.84MB
13.Hybrid Approaches/1301.Hybrid Recommenders and Exercise.mp4
8.82MB
13.Hybrid Approaches/1302.Exercise Solution Hybrid Recommenders.mp4
20.42MB
14.Wrapping Up/1401.More to Explore.mp4
61.91MB
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