udemy-building-recommender-systems-with-machine-learning-and-ai-2020-11

Started\ 0:00 Udemy 101 Getting the Most From Th 2:10 [Activity] Install Anaconda, cours 11:16 Course Roadmap 15:09 What Is a Recommender System 17:57 Types of Recommenders 21:19 Understanding You through Implici 25:45 Top-N Recommender Architecture 31:38 [Quiz] Review the basics of recom to Python [Optional]\ 36:25 [Activity] The Basics of Python 41:29 Data Structures in Python 46:46 Functions in Python 49:32 [Exercise] Booleans, loops, and a Recommender Systems\ 53:24 TrainTest and Cross Validation 57:14 Accuracy Metrics (RMSE, MAE) 1:01:20 Top-N Hit Rate - Many Ways 1:05:55 Coverage, Diversity, and Novelt 1:10:51 Churn, Responsiveness, and AB T 1:15:58 [Quiz] Review ways to measure y 1:18:54 [Activity] Walkthrough of Recom 1:25:47 [Activity] Walkthrough of TestM 1:30:56 [Activity] Measure the Performa 4.A Recommender Engine Framework\ 1:33:21 Our Recommender Engine Architec 1:40:48 [Activity] Recommender Engine W 1:48:35 [Activity] Review the Results o Filtering\ 1:51:46 Content-Based Recommendations, 2:00:45 K-Nearest-Neighbors and Content 2:04:44 [Activity] Producing and Evalua 2:10:08 A Note on Using Implicit Rating 2:13:45 [Activity] Bleeding Edge Alert! 2:18:16 [Exercise] Dive Deeper into Con Collaborative Filt 2:22:42 Measuring Similarity, and Spars 2:27:32 Similarity Metrics 2:36:04 User-based Collaborative Filter 2:43:29 [Activity] User-based Collabora 2:48:28 Item-based Collaborative Filter 2:52:43 [Activity] Item-based Collabora 2:55:07 [Exercise] Tuning Collaborative 2:58:38 [Activity] Evaluating Collabora 3:00:07 [Exercise] Measure the Hit Rate 3:02:25 KNN Recommenders 3:06:29 [Activity] Running User and Ite 3:08:55 [Exercise] Experiment with diff 3:13:20 Bleeding Edge Alert! Translatio Factorization Methods\ 3:15:50 Principal Component Analysis (P 3:22:22 Singular Value Decomposition 3:29:18 [Activity] Running SVD and SVD 3:33:05 Improving on SVD 3:37:39 [Exercise] Tune the hyperparame 3:39:37 Bleeding Edge Alert! Sparse Lin to Deep Learning [Option 3:43:08 Deep Learning Introduction 3:44:38 Deep Learning Pre-Requisites 3:52:52 History of Artificial Neural Ne 4:03:43 [Activity] Playing with Tensorf 4:15:45 Training Neural Networks 4:21:33 Tuning Neural Networks 4:25:25 Activation Functions More Depth 4:36:01 Introduction to Tensorflow 4:47:31 [Activity] Handwriting Recognit 5:12:54 Introduction to Keras 5:15:42 [Activity] Handwriting Recognit 5:25:35 Classifier Patterns with Keras 5:29:34 [Exercise] Predict Political Pa 5:39:29 Intro to Convolutional Neural N 5:48:28 CNN Architectures 5:51:23 [Activity] Handwriting Recognit 6:00:01 Intro to Recurrent Neural Netwo 6:07:39 Training Recurrent Neural Netwo 6:11:01 [Activity] Sentiment Analysis o 6:22:03 Tuning Neural Networks 6:26:42 Neural Network Regularization T Learning for Recommender Systems 6:33:03 Intro to Deep Learning for Reco 6:35:22 Restricted Boltzmann Machines ( 6:43:25 [Activity] Recommendations with 6:56:12 [Activity] Recommendations with 7:03:23 [Activity] Evaluating the RBM R 7:07:07 [Exercise] Tuning Restricted Bo 7:08:50 Exercise Results Tuning a RBM R 7:10:06 Auto-Encoders for Recommendatio 7:14:33 [Activity] Recommendations with 7:21:56 Clickstream Recommendations wit 7:29:20 [Exercise] Get GRU4Rec Working 7:32:02 Exercise Results GRU4Rec in Act 7:39:54 Bleeding Edge Alert! Deep Facto 7:45:43 More Emerging Tech to Watch it Up\ 7:50:58 [Activity] Introduction and Installation of Apache Spark 7:56:48 Apache Spark Architecture 8:02:01 [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS 8:08:04 [Activity] Recommendations from 20 million ratings with Spark 8:13:01 Amazon DSSTNE 8:17:43 DSSTNE in Action 8:27:08 Scaling Up DSSTNE 8:29:23 AWS SageMaker and Factorization Machines 8:33:48 SageMaker in Action Factorization Machines on one million ratings, in the cloud 8:41:27 Other Systems of Note (Amazon Personalize, RichRelevance, Recombee, and more) 8:51:57 Recommender System Architecture Challenges of Recommender Systems\ 9:02:11 The Cold Start Problem (and solutions) 9:08:23 [Exercise] Implement Random Exploration 9:09:18 Exercise Solution Random Exploration 9:11:36 Stoplists 9:16:24 [Exercise] Implement a Stoplist 9:16:57 Exercise Solution Implement a Stoplist 9:19:20 Filter Bubbles, Trust, and Outliers 9:24:59 [Exercise] Identify and Eliminate Outlier Users 9:25:44 Exercise Solution Outlier Removal 9:29:44 Fraud, The Perils of Clickstream, and International Concerns 9:34:18 Temporal Effects, and Value-Aware Recommendations Studies\ 9:37:49 Case Study YouTube, Part 1-2 9:48:36 Case Study Netflix, Part 1-2 Approaches\ 9:56:31 Hybrid Recommenders and Exercise 9:59:25 Exercise Solution Hybrid Recommenders Up\ 10:03:43 More to Explore