Video description
Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.
About This Video
- Learn how to build recommender systems from one of Amazon's pioneers in the field
- This comprehensive course takes you all the way from the early days of collaborative filtering to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user
In Detail
Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation technologies. You've seen automated recommendations everywhere—on Netflix's home page, on YouTube, and on Amazon–as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them. We cover tried-and-true recommendation algorithms based on neighborhood-based collaborative filtering and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from Kane's extensive industry experience and understand the real-world challenges you'll encounter when applying these algorithms at a large scale and with real-world data. The coding exercises in this course use the Python programming language. We include an intro to Python if you're new to it, but you'll need some prior programming experience in order to use this course successfully. We also include a short introduction to deep learning if you are new to the field of artificial intelligence, but you'll need to be able to understand new computer algorithms. Hope to see you in the course soon!
Publisher resources
Table of contents
- Chapter 1 : Getting Started
- Chapter 2 : Introduction to Python
-
Chapter 3 : Evaluating Recommender Systems
- Train/Test and Cross Validation
- Accuracy Metrics (RMSE, MAE)
- Top-N Hit Rate - Many Ways
- Coverage, Diversity, and Novelty
- Churn, Responsiveness, and A/B Tests
- Review ways to measure your recommender.
- Walkthrough of RecommenderMetrics.py
- Walkthrough of TestMetrics.py
- Measure the Performance of SVD Recommendations
- Chapter 4 : A Recommender Engine Framework
- Chapter 5 : Content-Based Filtering
-
Chapter 6 : Neighborhood-Based Collaborative Filtering
- Measuring Similarity, and Sparsity
- Similarity Metrics
- User-based Collaborative Filtering
- User-based Collaborative Filtering, Hands-On
- Item-based Collaborative Filtering
- Item-based Collaborative Filtering, Hands-On
- Tuning Collaborative Filtering Algorithms
- Evaluating Collaborative Filtering Systems Offline
- Measure the Hit Rate of Item-Based Collaborative Filtering
- KNN Recommenders
- Running User and Item-Based KNN on MovieLens
- Experiment with different KNN parameters.
- Bleeding Edge Alert! Translation-Based Recommendations
- Chapter 7 : Matrix Factorization Methods
-
Chapter 8 : Introduction to Deep Learning
- Deep Learning Introduction
- Deep Learning Pre-Requisites
- History of Artificial Neural Networks
- [Activity] Playing with Tensorflow
- Training Neural Networks
- Tuning Neural Networks
- Introduction to Tensorflow
- [Activity] Handwriting Recognition with Tensorflow, part 1
- [Activity] Handwriting Recognition with Tensorflow, part 2
- Introduction to Keras
- [Activity] Handwriting Recognition with Keras
- Classifier Patterns with Keras
- [Exercise] Predict Political Parties of Politicians with Keras
- Intro to Convolutional Neural Networks (CNN's)
- CNN Architectures
- [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs)
- Intro to Recurrent Neural Networks (RNN's)
- Training Recurrent Neural Networks
- [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras
-
Chapter 9 : Deep Learning for Recommender Systems
- Intro to Deep Learning for Recommenders
- Restricted Boltzmann Machines (RBM's)
- [Activity] Recommendations with RBM's, part 1
- [Activity] Recommendations with RBM's, part 2
- [Activity] Evaluating the RBM Recommender
- [Exercise] Tuning Restricted Boltzmann Machines
- Exercise Results: Tuning a RBM Recommender
- Auto-Encoders for Recommendations: Deep Learning for Recs
- [Activity] Recommendations with Deep Neural Networks
- Clickstream Recommendations with RNN's
- [Exercise] Get GRU4Rec Working on your Desktop
- Exercise Results: GRU4Rec in Action
- Bleeding Edge Alert! Deep Factorization Machines
- More Emerging Tech to Watch
-
Chapter 10 : Scaling it up
- [Activity] Introduction and Installation of Apache Spark
- Apache Spark Architecture
- [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS
- [Activity] Recommendations from 20 million ratings with Spark
- Amazon DSSTNE
- DSSTNE in Action
- Scaling Up DSSTNE
- AWS SageMaker and Factorization Machines
- SageMaker in Action: Factorization Machines on one million ratings, in the cloud
-
Chapter 11 : Real-World Challenges of Recommender Systems
- The Cold Start Problem (and solutions)
- [Exercise] Implement Random Exploration
- Exercise Solution: Random Exploration
- Stoplists
- [Exercise] Implement a Stoplist
- Exercise Solution: Implement a Stoplist
- Filter Bubbles, Trust, and Outliers
- [Exercise] Identify and Eliminate Outlier Users
- Exercise Solution: Outlier Removal
- Fraud, the Perils of Clickstream, and International Concerns
- Temporal Effects, and Value-Aware Recommendations
- Chapter 12 : Case Studies
- Chapter 13 : Hybrid Approaches
- Chapter 14 : Wrapping Up
Product information
- Title: Building Recommender Systems with Machine Learning and AI
- Author(s):
- Release date: January 2021
- Publisher(s): Packt Publishing
- ISBN: 9781789803273
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