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Recommendation Systems on Google Cloud

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Overview

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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

Syllabus

  • Welcome to Recommendation Systems on Google Cloud
    • Welcome to Recommendation Systems on Google Cloud
  • Recommendation Systems Overview
    • Introduction
    • Types of Recommendation Systems
    • Content-Based or Collaborative
    • Recommendation System Pitfalls
    • Discussion
    • Reading: Recommendation Systems Overview
    • Recommendation Systems Overview
  • Content-Based Recommendation Systems
    • Content-Based Recommendation Systems
    • Similarity Measures
    • Building a User Vector
    • Making Recommendations Using a User Vector
    • Making Recommendations for Many Users
    • Lab intro: Create a Content-Based Recommendation System
    • Implementing a Content-Based Filtering using Low Level TensorFlow Operations
    • Using Neural Networks for Content-Based Recommendation Systems
    • Summary
    • Readings: Content-Based Recommendation Systems
    • Content-Based Recommendation Systems Quiz
  • Collaborative Filtering Recommendations Systems
    • Types of User Feedback Data
    • Embedding Users and Items
    • Factorization Approaches
    • The ALS Algorithm
    • Preparing Input Data for ALS
    • Creating Sparse Tensors For Efficient WALS Input
    • Instantiating a WALS Estimator: From Input to Estimator
    • Instantiating a WAL Estimator: Decoding TFRecords
    • Instantiating a WALS Estimator: Recovering Keys
    • Instantiating a WALS Estimator: Training and Prediction
    • Lab Intro: Collaborative Filtering with Google Analytics Data
    • Issues with Collaborative Filtering
    • Cold Starts
    • Readings: Collaborative Filtering Recommendations Systems
    • Collaborative Filtering Recommendations Systems
  • Neural Networks for Recommendation Systems
    • Hybrid Recommendation Systems
    • Lab Intro: Designing a Hybrid Recommendation System
    • Lab Intro: Designing a Hybrid Collaborative Filtering Recommendation System
    • Lab Intro: Designing a Hybrid Knowledge-based Recommendation System
    • Lab Intro: Building a Neural Network Hybrid Recommendation System
    • ML on GCP: Hybrid Recommendations with the MovieLens Dataset
    • Context-Aware Recommendation Systems
    • Context-Aware Algorithms
    • Contextual Postfiltering
    • Modeling Using Context-Aware Algorithms
    • YouTube Recommendation System Case Study: Overview
    • YouTube Recommendation System Case Study: Candidate Generation
    • YouTube Recommendation System Case Study: Ranking
    • Summary
    • Readings: Neural Networks for Recommendation Systems
    • Neural Networks for Recommendations
  • Reinforcement Learning
    • Introduction to module
    • Introduction to Reinforcement Learning
    • The reinforcement learning framework and workflow
    • Model-based and model-free reinforcement learning
    • Value-based reinforcement learning
    • Policy-based reinforcement learning
    • Contextual bandits
    • Applications of reinforcement learning
    • Reinforcement Learning
    • Readings: Reinforcement Learning
    • Lab Intro
    • Applying Contextual Bandits for Recommendations with Tensorflow and TF-Agents
    • Lab Review
  • Summary
    • Course Summary
    • Specialization Summary
    • All Course Readings
  • Your Next Steps
    • Claim credential

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