Канал: AI-First

2.4.1. Lab Intro: Cloud Composer

2.4.1. Lab Intro: Cloud Composer

2.4.2. Lab Intro: End-to-End Recommendation System

2.4.2. Lab Intro: End-to-End Recommendation System

2.5.3. Course Summary

2.5.3. Course Summary

2.2.4. Modelling Using Context-Aware Algorithms

2.2.4. Modelling Using Context-Aware Algorithms

2.2.6. Youtube Recommendation System Case Study: Candidate Generation

2.2.6. Youtube Recommendation System Case Study: Candidate Generation

2.1.1. Hybrid Recommendation Systems

2.1.1. Hybrid Recommendation Systems

2.3.4. Cloud Composer: DAGs

2.3.4. Cloud Composer: DAGs

2.3.2. Architecture Overview

2.3.2. Architecture Overview

2.3.1. Introduction to End-to-End Recommendation System

2.3.1. Introduction to End-to-End Recommendation System

2.1.3. Lab: Designing a Hybrid Collaborative Filtering Recommendation Systems

2.1.3. Lab: Designing a Hybrid Collaborative Filtering Recommendation Systems

2.2.3. Contextual Postfiltering

2.2.3. Contextual Postfiltering

2.3.5. Cloud Composer: Operators for ML

2.3.5. Cloud Composer: Operators for ML

2.2.2 Context-Aware Algorithms

2.2.2 Context-Aware Algorithms

2.2.1. Context-Aware Recommendation Systems

2.2.1. Context-Aware Recommendation Systems

2.2.8. Summary

2.2.8. Summary

2.2.5. Youtube Recommendation System Case Study: Overview

2.2.5. Youtube Recommendation System Case Study: Overview

2.3.6. Cloud Composer: Scheduling

2.3.6. Cloud Composer: Scheduling

2.1.5. Lab Intro: Building a Neural Network Hybrid Recommendation Systems

2.1.5. Lab Intro: Building a Neural Network Hybrid Recommendation Systems

2.1.6. Lab Solution: Building a Neural Network Hybrid Recommendation System

2.1.6. Lab Solution: Building a Neural Network Hybrid Recommendation System

2.1.4. Lab: Designing a Hybrid Knowledge-based Recommendation Systems

2.1.4. Lab: Designing a Hybrid Knowledge-based Recommendation Systems

2.3.8. Cloud Composer: Monitoring and Logging

2.3.8. Cloud Composer: Monitoring and Logging

2.3.3. Cloud Composer Overview

2.3.3. Cloud Composer Overview

2.1.2. Lab: Designing a Hybrid Recommendation Systems

2.1.2. Lab: Designing a Hybrid Recommendation Systems

2.3.7. Cloud Composer: Triggering Workflows with Cloud Functions

2.3.7. Cloud Composer: Triggering Workflows with Cloud Functions

2.2.7. Youtube Recommendation System Case Study: Ranking

2.2.7. Youtube Recommendation System Case Study: Ranking

1.5.10. Cold Starts

1.5.10. Cold Starts

1.5.2. Instantiating a WALS Estimator: From Input to Estimator

1.5.2. Instantiating a WALS Estimator: From Input to Estimator

1.5.1. Creating Sparse Tensors For Efficient WALS Input

1.5.1. Creating Sparse Tensors For Efficient WALS Input

1.5.6. Lab Intro: Collaborative Filtering with Google Analytics Data

1.5.6. Lab Intro: Collaborative Filtering with Google Analytics Data

1.5.5. Instaniating a WALS Estimator: Training and Prediction

1.5.5. Instaniating a WALS Estimator: Training and Prediction

1.5.9. Productionized WALS

1.5.9. Productionized WALS

1.5.3. Instantiating a WALS Estimator: Decoding TFRecords

1.5.3. Instantiating a WALS Estimator: Decoding TFRecords

1.5.7. Lab Solution: Collaborative Filtering with Google Analytics Data

1.5.7. Lab Solution: Collaborative Filtering with Google Analytics Data

1.5.8. Issues with Collaborative Filtering

1.5.8. Issues with Collaborative Filtering

1.5.4. Instantiating a WALS Estimator: Recovering Keys

1.5.4. Instantiating a WALS Estimator: Recovering Keys

1.4.4. The ALS Algorithm

1.4.4. The ALS Algorithm

1.4.3. Factorization Approaches

1.4.3. Factorization Approaches

1.4.5. Preparing Input Data for ALS

1.4.5. Preparing Input Data for ALS

1.4.2. Embedding Users and Items

1.4.2. Embedding Users and Items

1.4.1. Types of Users Feedback Data

1.4.1. Types of Users Feedback Data

1.3.3. Lab Solution: Create a Content-Based Recommendation System Using a Neural Network

1.3.3. Lab Solution: Create a Content-Based Recommendation System Using a Neural Network

1.3.2. Lab Intro: Create a Content-Based Recommendation system Using a Neural Network

1.3.2. Lab Intro: Create a Content-Based Recommendation system Using a Neural Network

1.3.1. Using Neural Networks for Content-Based Recommendation Systems

1.3.1. Using Neural Networks for Content-Based Recommendation Systems

1.2.6. Lab intro: Create a Content-Based Recommendation System

1.2.6. Lab intro: Create a Content-Based Recommendation System

1.2.7. Lab Solution: Create a Conten-Bases Recommendation System

1.2.7. Lab Solution: Create a Conten-Bases Recommendation System

1.2.5. Making Recommendations for Many Users

1.2.5. Making Recommendations for Many Users

1.2.4. Making Recommendations Using a User Vector

1.2.4. Making Recommendations Using a User Vector

1.2.3. Building a user Vector

1.2.3. Building a user Vector

1.2.2. Similarity Measures

1.2.2. Similarity Measures

1.2.1. Content-Based Recommendation Systems

1.2.1. Content-Based Recommendation Systems

1.1.4. Recommendation System Pitfalls

1.1.4. Recommendation System Pitfalls

1.1.2. Types of Recommendation Systems

1.1.2. Types of Recommendation Systems

1.1.5. Discussion

1.1.5. Discussion

1.1.3. Content-Bases or Collaborative

1.1.3. Content-Bases or Collaborative

1.1.1. Recommendation systems overview

1.1.1. Recommendation systems overview

1.0. Introduction to Recommendation system by Google

1.0. Introduction to Recommendation system by Google