Recommendation Systems with TensorFlow on GCP

By Google Cloud
In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
Course info
Level
Advanced
Updated
Oct 29, 2019
Duration
6h 46m
Table of contents
Recommendation Systems Overview
Introduction 9m Getting started with GCP and Qwiklabs 4m Introduction 6m Types of Recommendation Systems 3m Content-Based or Collaborative 6m Recommendation System Pitfalls 4m Discussion 2m
Content-Based Recommendation Systems
Content-Based Recommendation Systems 2m Similarity Measures 3m Building a User Vector 4m Making Recommendations Using a User Vector 2m Making Recommendations for Many Users 7m Lab intro: Create a Content-Based Recommendation System 0m Lab: Content-Based Filtering by Hand 0m Lab Solution:Create a Content-Based Recommendation System 14m Using Neural Networks for Content-Based Recommendation Systems 4m Lab Intro: Create a Content-Based Recommendation System Using a Neural Network 1m Lab: Content-Based Filtering using Neural Networks 0m Lab Solution:Create a Content-Based Recommendation System Using a Neural Network 37m
COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS
Types of User Feedback Data 7m Embedding Users and Items 13m Factorization Approaches 7m The ALS Algorithm 5m Preparing Input Data for ALS 6m Creating Sparse Tensors For Efficient WALS Input 3m Instantiating a WALS Estimator:From Input to Estimator 5m Instantiating a WAL Estimator:Decoding TFRecords 4m Instantiating a WALS Estimator:Recovering Keys 13m Instantiating a WALS Estimator:Training and Prediction 6m Lab Intro:Collaborative Filtering with Google Analytics Data 1m Lab: Collaborative Filtering on Google Analytics data 0m Lab Solution:Collaborative Filtering with Google Analytics Data 44m Issues with Collaborative Filtering 4m Productionized WALS Demo 22m Cold Starts 6m
Neural Networks for Recommendation Systems
Hybrid Recommendation Systems 15m Lab:Designing a Hybrid Recommendation System 6m Lab:Designing a Hybrid Collaborative Filtering Recommendation System 4m Lab:Designing a Hybrid Knowledge-based Recommendation System 5m Lab Intro: Building a Neural Network Hybrid Recommendation System 0m Lab: Neural network hybrid recommendation system on Google Analytics 0m Lab Solution:Building a Neural Network Hybrid Recommendation System 35m Context-Aware Recommendation Systems 8m Context-Aware Algorithms 5m Contextual Postfiltering 3m Modeling Using Context-Aware Algorithms 5m YouTube Recommendation System Case Study:Overview 3m YouTube Recommendation System Case Study:Candidate Generation 3m YouTube Recommendation System Case Study:Ranking 3m Summary 2m
Building an End-to-End Recommendation System
Introduction 4m Architecture Overview 3m Cloud Composer Overview 16m Cloud Composer:DAGs 5m Cloud Composer:Operators for ML 9m Cloud Composer:Scheduling 2m Cloud Composer:Triggering Workflows with Cloud Functions 4m Cloud Composer:Monitoring and Logging 5m Lab Intro: End-to-End Recommendation System 1m Lab: End to End Recommendation System 0m
Summary
Course Summary 1 3m Course Summary 2 5m
Description
Course info
Level
Advanced
Updated
Oct 29, 2019
Duration
6h 46m
Description

In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.

About the author
Google Cloud
About the author

Build, innovate, and scale with Google Cloud Platform.

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