CourseClub NET Packtpub Building Recommender Systems with Machine Learning and AI

CourseClub NET Packtpub Building Recommender Systems with Machine Learning and AI

Name	[CourseClub.NET] Packtpub - Building Recommender Systems with Machine Learning and AI	DOWNLOAD Copy Link
Total Size	3.1 GB
Total Files	110
Last Seen	2024-10-03 01:38
Hash	333A3D99C556019529A3D9CA01FD159B5894792B

/01.Getting Started/
0101.Install Anaconda, course materials, and create movie recommendations!.mp4	92.4 MB
0102.Course Roadmap.mp4	72.6 MB
0103.Types of Recommenders.mp4	14.8 MB
0104.Understanding You through Implicit and Explicit Ratings.mp4	9.6 MB
0105.Top-N Recommender Architecture.mp4	16.1 MB
0106.Review the basics of recommender systems..mp4	11.7 MB
/02.Introduction to Python/
0201.The Basics of Python.mp4	44.0 MB
0202.Data Structures in Python.mp4	12.2 MB
0203.Functions in Python.mp4	6.1 MB
0204.Booleans, loops, and a hands-on challenge.mp4	7.7 MB
/03.Evaluating Recommender Systems/
0301.TrainTest and Cross Validation.mp4	24.3 MB
0302.Accuracy Metrics (RMSE, MAE).mp4	49.0 MB
0303.Top-N Hit Rate - Many Ways.mp4	12.7 MB
0304.Coverage, Diversity, and Novelty.mp4	8.3 MB
0305.Churn, Responsiveness, and AB Tests.mp4	86.7 MB
0306.Review ways to measure your recommender..mp4	8.7 MB
0307.Walkthrough of RecommenderMetrics.py.mp4	40.7 MB
0308.Walkthrough of TestMetrics.py.mp4	26.6 MB
0309.Measure the Performance of SVD Recommendations.mp4	12.6 MB
/04.A Recommender Engine Framework/
0401.Our Recommender Engine Architecture.mp4	19.1 MB
0402.Recommender Engine Walkthrough, Part 1.mp4	19.5 MB
0403.Recommender Engine Walkthrough, Part 2.mp4	19.5 MB
0404.Review the Results of our Algorithm Evaluation..mp4	15.0 MB
/05.Content-Based Filtering/
0501.Content-Based Recommendations, and the Cosine Similarity Metric.mp4	40.3 MB
0502.K-Nearest-Neighbors and Content Recs.mp4	12.4 MB
0503.Producing and Evaluating Content-Based Movie Recommendations.mp4	29.2 MB
0504.Bleeding Edge Alert! Mise en Scene Recommendations.mp4	35.3 MB
0505.Dive Deeper into Content-Based Recommendations.mp4	11.2 MB
/06.Neighborhood-Based Collaborative Filtering/
0601.Measuring Similarity, and Sparsity.mp4	73.1 MB
0602.Similarity Metrics.mp4	16.2 MB
0603.User-based Collaborative Filtering.mp4	21.0 MB
0604.User-based Collaborative Filtering, Hands-On.mp4	25.8 MB
0605.Item-based Collaborative Filtering.mp4	64.6 MB
0606.Item-based Collaborative Filtering, Hands-On.mp4	19.0 MB
0607.Tuning Collaborative Filtering Algorithms.mp4	10.5 MB
0608.Evaluating Collaborative Filtering Systems Offline.mp4	11.1 MB
0609.Measure the Hit Rate of Item-Based Collaborative Filtering.mp4	4.6 MB
0610.KNN Recommenders.mp4	22.9 MB
0611.Running User and Item-Based KNN on MovieLens.mp4	20.6 MB
0612.Experiment with different KNN parameters..mp4	40.7 MB
0613.Bleeding Edge Alert! Translation-Based Recommendations.mp4	20.6 MB
/07.Matrix Factorization Methods/
0701.Principal Component Analysis (PCA).mp4	68.1 MB
0702.Singular Value Decomposition.mp4	13.6 MB
0703.Running SVD and SVD++ on MovieLens.mp4	24.2 MB
0704.Improving on SVD.mp4	10.2 MB
0705.Tune the hyperparameters on SVD.mp4	8.4 MB
0706.Bleeding Edge Alert! Sparse Linear Methods (SLIM).mp4	22.1 MB
/08.Introduction to Deep Learning/
0801.Deep Learning Introduction.mp4	23.9 MB
0802.Deep Learning Pre-Requisites.mp4	21.1 MB
0803.History of Artificial Neural Networks.mp4	42.4 MB
0804.[Activity] Playing with Tensorflow.mp4	122.6 MB
0805.Training Neural Networks.mp4	19.8 MB
0806.Tuning Neural Networks.mp4	13.7 MB
0807.Introduction to Tensorflow.mp4	45.1 MB
0808.[Activity] Handwriting Recognition with Tensorflow, part 1.mp4	97.4 MB
0809.[Activity] Handwriting Recognition with Tensorflow, part 2.mp4	28.7 MB
0810.Introduction to Keras.mp4	7.0 MB
0811.[Activity] Handwriting Recognition with Keras.mp4	49.2 MB
0812.Classifier Patterns with Keras.mp4	13.8 MB
0813.[Exercise] Predict Political Parties of Politicians with Keras.mp4	56.3 MB
0814.Intro to Convolutional Neural Networks (CNN_s).mp4	38.2 MB
0815.CNN Architectures.mp4	10.1 MB
0816.[Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp4	44.5 MB
0817.Intro to Recurrent Neural Networks (RNN_s).mp4	23.6 MB
0818.Training Recurrent Neural Networks.mp4	10.6 MB
0819.[Activity] Sentiment Analysis of Movie Reviews using RNN_s and Keras.mp4	76.9 MB
/09.Deep Learning for Recommender Systems/
0901.Intro to Deep Learning for Recommenders.mp4	58.7 MB
0902.Restricted Boltzmann Machines (RBM_s).mp4	16.7 MB
0903.[Activity] Recommendations with RBM_s, part 1.mp4	53.0 MB
0904.[Activity] Recommendations with RBM_s, part 2.mp4	27.7 MB
0905.[Activity] Evaluating the RBM Recommender.mp4	20.8 MB
0906.[Exercise] Tuning Restricted Boltzmann Machines.mp4	56.3 MB
0907.Exercise Results Tuning a RBM Recommender.mp4	7.0 MB
0908.Auto-Encoders for Recommendations Deep Learning for Recs.mp4	12.4 MB
0909.[Activity] Recommendations with Deep Neural Networks.mp4	39.0 MB
0910.Clickstream Recommendations with RNN_s.mp4	26.1 MB
0911.[Exercise] Get GRU4Rec Working on your Desktop.mp4	4.1 MB
0912.Exercise Results GRU4Rec in Action.mp4	43.0 MB
0913.Bleeding Edge Alert! Deep Factorization Machines.mp4	46.5 MB
0914.More Emerging Tech to Watch.mp4	14.9 MB
/10.Scaling it up/
1001.[Activity] Introduction and Installation of Apache Spark.mp4	42.0 MB
1002.Apache Spark Architecture.mp4	9.8 MB
1003.[Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.mp4	24.9 MB
1004.[Activity] Recommendations from 20 million ratings with Spark.mp4	28.2 MB
1005.Amazon DSSTNE.mp4	43.4 MB
1006.DSSTNE in Action.mp4	64.1 MB
1007.Scaling Up DSSTNE.mp4	5.0 MB
1008.AWS SageMaker and Factorization Machines.mp4	8.3 MB
1009.SageMaker in Action Factorization Machines on one million ratings, in the cloud.mp4	46.3 MB
/11.11 Real-World Challenges of Recommender Systems/
1101.The Cold Start Problem (and solutions).mp4	12.4 MB
1102.[Exercise] Implement Random Exploration.mp4	1.3 MB
1103.Exercise Solution Random Exploration.mp4	16.2 MB
1104.Stoplists.mp4	9.1 MB
1105.[Exercise] Implement a Stoplist.mp4	780.1 KB
1106.Exercise Solution Implement a Stoplist.mp4	15.8 MB
1107.Filter Bubbles, Trust, and Outliers.mp4	22.8 MB
1108.[Exercise] Identify and Eliminate Outlier Users.mp4	1.0 MB
1109.Exercise Solution Outlier Removal.mp4	17.4 MB
1110.Fraud, the Perils of Clickstream, and International Concerns.mp4	76.3 MB
1111.Temporal Effects, and Value-Aware Recommendations.mp4	85.6 MB
/12.Case Studies/
1201.Case Study YouTube, Part 1.mp4	13.4 MB
1202.Case Study YouTube, Part 2.mp4	13.1 MB
1203.Case Study Netflix, Part 1.mp4	14.5 MB
1204.Case Study Netflix, Part 2.mp4	10.3 MB
/13.Hybrid Approaches/
1301.Hybrid Recommenders and Exercise.mp4	9.2 MB
1302.Exercise Solution Hybrid Recommenders.mp4	21.4 MB
/14.Wrapping Up/
1401.More to Explore.mp4	64.9 MB
/Exercise Files/
exercise_files.zip	1.8 MB
/
[CourseClub.NET].url	0.1 KB
[DesireCourse.Com].url	0.1 KB
Total files 110