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
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.
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