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Building Recommender Systems with Machine Learning and AI
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Building Recommender Systems with Machine Learning and AI (Size: 4.45 GB) (Files: 223)
Building Recommender Systems with Machine Learning and AI
8. Introduction to Deep Learning [Optional]
4. [Activity] Playing with Tensorflow.mp4
1. Deep Learning Introduction.mp4
1. Deep Learning Introduction.srt
2. Deep Learning Pre-Requisites.mp4
2. Deep Learning Pre-Requisites.srt
3. History of Artificial Neural Networks.mp4
3. History of Artificial Neural Networks.srt
4. [Activity] Playing with Tensorflow.srt
5. Training Neural Networks.mp4
5. Training Neural Networks.srt
6. Tuning Neural Networks.mp4
6. Tuning Neural Networks.srt
7. Introduction to Tensorflow.mp4
7. Introduction to Tensorflow.srt
8. [Activity] Handwriting Recognition with Tensorflow, part 1.mp4
8. [Activity] Handwriting Recognition with Tensorflow, part 1.srt
9. [Activity] Handwriting Recognition with Tensorflow, part 2.mp4
9. [Activity] Handwriting Recognition with Tensorflow, part 2.srt
10. [Activity] Handwriting Recognition with Tensorflow, Part 3.mp4
10. [Activity] Handwriting Recognition with Tensorflow, Part 3.srt
11. Introduction to Keras.mp4
11. Introduction to Keras.srt
12. [Activity] Handwriting Recognition with Keras.mp4
12. [Activity] Handwriting Recognition with Keras.srt
13. Classifier Patterns with Keras.mp4
13. Classifier Patterns with Keras.srt
14. [Exercise] Predict Political Parties of Politicians with Keras.mp4
14. [Exercise] Predict Political Parties of Politicians with Keras.srt
15. Intro to Convolutional Neural Networks (CNN's).mp4
15. Intro to Convolutional Neural Networks (CNN's).srt
16. CNN Architectures.mp4
16. CNN Architectures.srt
17. [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp4
17. [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).srt
18. Intro to Recurrent Neural Networks (RNN's).mp4
18. Intro to Recurrent Neural Networks (RNN's).srt
19. Training Recurrent Neural Networks.mp4
19. Training Recurrent Neural Networks.srt
20. [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras.mp4
20. [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras.srt
ReadMe.txt
Visit Coursedrive.org.url
1. Getting Started
1. Udemy 101 Getting the Most From This Course.mp4
1. Udemy 101 Getting the Most From This Course.srt
2. [Activity] Install Anaconda, course materials, and create movie recommendations!.mp4
2. [Activity] Install Anaconda, course materials, and create movie recommendations!.srt
3. Course Roadmap.mp4
3. Course Roadmap.srt
4. Types of Recommenders.mp4
4. Types of Recommenders.srt
5. Understanding You through Implicit and Explicit Ratings.mp4
5. Understanding You through Implicit and Explicit Ratings.srt
6. Top-N Recommender Architecture.mp4
6. Top-N Recommender Architecture.srt
7. [Quiz] Review the basics of recommender systems..mp4
7. [Quiz] Review the basics of recommender systems..srt
2. Introduction to Python [Optional]
1. [Activity] The Basics of Python.mp4
1. [Activity] The Basics of Python.srt
2. Data Structures in Python.mp4
2. Data Structures in Python.srt
3. Functions in Python.mp4
3. Functions in Python.srt
4. [Exercise] Booleans, loops, and a hands-on challenge.mp4
4. [Exercise] Booleans, loops, and a hands-on challenge.srt
3. Evaluating Recommender Systems
1. TrainTest and Cross Validation.mp4
1. TrainTest and Cross Validation.srt
2. Accuracy Metrics (RMSE, MAE).mp4
2. Accuracy Metrics (RMSE, MAE).srt
3. Top-N Hit Rate - Many Ways.mp4
3. Top-N Hit Rate - Many Ways.srt
4. Coverage, Diversity, and Novelty.mp4
4. Coverage, Diversity, and Novelty.srt
5. Churn, Responsiveness, and AB Tests.mp4
5. Churn, Responsiveness, and AB Tests.srt
6. [Quiz] Review ways to measure your recommender..mp4
6. [Quiz] Review ways to measure your recommender..srt
7. [Activity] Walkthrough of RecommenderMetrics.py.mp4
7. [Activity] Walkthrough of RecommenderMetrics.py.srt
8. [Activity] Walkthrough of TestMetrics.py.mp4
8. [Activity] Walkthrough of TestMetrics.py.srt
9. [Activity] Measure the Performance of SVD Recommendations.mp4
9. [Activity] Measure the Performance of SVD Recommendations.srt
4. A Recommender Engine Framework
1. Our Recommender Engine Architecture.mp4
1. Our Recommender Engine Architecture.srt
2. [Activity] Recommender Engine Walkthrough, Part 1.mp4
2. [Activity] Recommender Engine Walkthrough, Part 1.srt
3. [Activity] Recommender Engine Walkthrough, Part 2.mp4
3. [Activity] Recommender Engine Walkthrough, Part 2.srt
4. [Activity] Review the Results of our Algorithm Evaluation..mp4
4. [Activity] Review the Results of our Algorithm Evaluation..srt
5. Content-Based Filtering
1. Content-Based Recommendations, and the Cosine Similarity Metric.mp4
1. Content-Based Recommendations, and the Cosine Similarity Metric.srt
2. K-Nearest-Neighbors and Content Recs.mp4
2. K-Nearest-Neighbors and Content Recs.srt
3. [Activity] Producing and Evaluating Content-Based Movie Recommendations.mp4
3. [Activity] Producing and Evaluating Content-Based Movie Recommendations.srt
4. [Activity] Bleeding Edge Alert! Mise en Scene Recommendations.mp4
4. [Activity] Bleeding Edge Alert! Mise en Scene Recommendations.srt
5. [Exercise] Dive Deeper into Content-Based Recommendations.mp4
5. [Exercise] Dive Deeper into Content-Based Recommendations.srt
6. Neighborhood-Based Collaborative Filtering
1. Measuring Similarity, and Sparsity.mp4
1. Measuring Similarity, and Sparsity.srt
2. Similarity Metrics.mp4
2. Similarity Metrics.srt
3. User-based Collaborative Filtering.mp4
3. User-based Collaborative Filtering.srt
4. [Activity] User-based Collaborative Filtering, Hands-On.mp4
4. [Activity] User-based Collaborative Filtering, Hands-On.srt
5. Item-based Collaborative Filtering.mp4
5. Item-based Collaborative Filtering.srt
6. [Activity] Item-based Collaborative Filtering, Hands-On.mp4
6. [Activity] Item-based Collaborative Filtering, Hands-On.srt
7. [Exercise] Tuning Collaborative Filtering Algorithms.mp4
7. [Exercise] Tuning Collaborative Filtering Algorithms.srt
8. [Activity] Evaluating Collaborative Filtering Systems Offline.mp4
8. [Activity] Evaluating Collaborative Filtering Systems Offline.srt
9. [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering.mp4
9. [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering.srt
10. KNN Recommenders.mp4
10. KNN Recommenders.srt
11. [Activity] Running User and Item-Based KNN on MovieLens.mp4
11. [Activity] Running User and Item-Based KNN on MovieLens.srt
12. [Exercise] Experiment with different KNN parameters..mp4
12. [Exercise] Experiment with different KNN parameters..srt
13. Bleeding Edge Alert! Translation-Based Recommendations.mp4
13. Bleeding Edge Alert! Translation-Based Recommendations.srt
7. Matrix Factorization Methods
1. Principal Component Analysis (PCA).mp4
1. Principal Component Analysis (PCA).srt
2. Singular Value Decomposition.mp4
2. Singular Value Decomposition.srt
3. [Activity] Running SVD and SVD++ on MovieLens.mp4
3. [Activity] Running SVD and SVD++ on MovieLens.srt
4. Improving on SVD.mp4
4. Improving on SVD.srt
5. [Exercise] Tune the hyperparameters on SVD.mp4
5. [Exercise] Tune the hyperparameters on SVD.srt
6. Bleeding Edge Alert! Sparse Linear Methods (SLIM).mp4
6. Bleeding Edge Alert! Sparse Linear Methods (SLIM).srt
9. Deep Learning for Recommender Systems
1. Intro to Deep Learning for Recommenders.mp4
1. Intro to Deep Learning for Recommenders.srt
2. Restricted Boltzmann Machines (RBM's).mp4
2. Restricted Boltzmann Machines (RBM's).srt
3. [Activity] Recommendations with RBM's, part 1.mp4
3. [Activity] Recommendations with RBM's, part 1.srt
4. [Activity] Recommendations with RBM's, part 2.mp4
4. [Activity] Recommendations with RBM's, part 2.srt
5. [Activity] Evaluating the RBM Recommender.mp4
5. [Activity] Evaluating the RBM Recommender.srt
6. [Exercise] Tuning Restricted Boltzmann Machines.mp4
6. [Exercise] Tuning Restricted Boltzmann Machines.srt
7. Exercise Results Tuning a RBM Recommender.mp4
7. Exercise Results Tuning a RBM Recommender.srt
8. Auto-Encoders for Recommendations Deep Learning for Recs.mp4
8. Auto-Encoders for Recommendations Deep Learning for Recs.srt
9. [Activity] Recommendations with Deep Neural Networks.mp4
9. [Activity] Recommendations with Deep Neural Networks.srt
10. Clickstream Recommendations with RNN's.mp4
10. Clickstream Recommendations with RNN's.srt
11. [Exercise] Get GRU4Rec Working on your Desktop.mp4
11. [Exercise] Get GRU4Rec Working on your Desktop.srt
12. Exercise Results GRU4Rec in Action.mp4
12. Exercise Results GRU4Rec in Action.srt
13. Bleeding Edge Alert! Deep Factorization Machines.mp4
13. Bleeding Edge Alert! Deep Factorization Machines.srt
14. More Emerging Tech to Watch.mp4
14. More Emerging Tech to Watch.srt
10. Scaling it Up
1. [Activity] Introduction and Installation of Apache Spark.mp4
1. [Activity] Introduction and Installation of Apache Spark.srt
2. Apache Spark Architecture.mp4
2. Apache Spark Architecture.srt
3. [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.mp4
3. [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.srt
4. [Activity] Recommendations from 20 million ratings with Spark.mp4
4. [Activity] Recommendations from 20 million ratings with Spark.srt
5. Amazon DSSTNE.mp4
5. Amazon DSSTNE.srt
6. DSSTNE in Action.mp4
6. DSSTNE in Action.srt
7. Scaling Up DSSTNE.mp4
7. Scaling Up DSSTNE.srt
8. AWS SageMaker and Factorization Machines.mp4
8. AWS SageMaker and Factorization Machines.srt
9. SageMaker in Action Factorization Machines on one million ratings, in the cloud.mp4
9. SageMaker in Action Factorization Machines on one million ratings, in the cloud.srt
11. Real-World Challenges of Recommender Systems
1. The Cold Start Problem (and solutions).mp4
1. The Cold Start Problem (and solutions).srt
2. [Exercise] Implement Random Exploration.mp4
2. [Exercise] Implement Random Exploration.srt
3. Exercise Solution Random Exploration.mp4
3. Exercise Solution Random Exploration.srt
4. Stoplists.mp4
4. Stoplists.srt
5. [Exercise] Implement a Stoplist.mp4
5. [Exercise] Implement a Stoplist.srt
6. Exercise Solution Implement a Stoplist.mp4
6. Exercise Solution Implement a Stoplist.srt
7. Filter Bubbles, Trust, and Outliers.mp4
7. Filter Bubbles, Trust, and Outliers.srt
8. [Exercise] Identify and Eliminate Outlier Users.mp4
8. [Exercise] Identify and Eliminate Outlier Users.srt
9. Exercise Solution Outlier Removal.mp4
9. Exercise Solution Outlier Removal.srt
10. Fraud, The Perils of Clickstream, and International Concerns.mp4
10. Fraud, The Perils of Clickstream, and International Concerns.srt
11. Temporal Effects, and Value-Aware Recommendations.mp4
11. Temporal Effects, and Value-Aware Recommendations.srt
12. Case Studies
1. Case Study YouTube, Part 1.mp4
1. Case Study YouTube, Part 1.srt
2. Case Study YouTube, Part 2.mp4
2. Case Study YouTube, Part 2.srt
3. Case Study Netflix, Part 1.mp4
3. Case Study Netflix, Part 1.srt
4. Case Study Netflix, Part 2.mp4
4. Case Study Netflix, Part 2.srt
13. Hybrid Approaches
1. Hybrid Recommenders and Exercise.mp4
1. Hybrid Recommenders and Exercise.srt
2. Exercise Solution Hybrid Recommenders.mp4
2. Exercise Solution Hybrid Recommenders.srt
14. Wrapping Up
1. More to Explore.mp4
1. More to Explore.srt
2. Bonus Lecture Discounts to continue your journey!.html
Visit Coursedrive.org.url
ReadMe.txt
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