Overview

Learn how to build recommender systems and help people discover new products and content with deep learning, neural networks, and machine learning recommendations.

Syllabus

1. Getting Started

Install Anaconda, review course materials, and create movie recommendations
Course roadmap
Understanding you through implicit and explicit ratings
Top-N recommender architecture
Review the basics of recommender systems

2. Introduction to Python

Data structures in Python
Functions in Python
Booleans, loops, and a hands-on challenge

3. Evaluating Recommender Systems

Train/test and cross-validation
Accuracy metrics (RMSE and 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

4. A Recommender Engine Framework

Our recommender engine architecture
Recommender engine walkthrough, part 1
Recommender engine walkthrough, part 2
Review the results of our algorithm evaluation

5. Content-Based Filtering

Content-based recommendations and the cosine similarity metric
K-nearest neighbors (KNN) and content recs
Producing and evaluating content-based movie recommendations
Bleeding edge alert: Mise-en-scene recommendations
Dive deeper into content-based recommendations

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

7. Matrix Factorization Methods

Principal component analysis (PCA)
Singular value decomposition (SVD)
Running SVD and SVD++ on MovieLens
Improving on SVD
Tune the hyperparameters on SVD
Bleeding edge alert: Sparse linear methods (SLIM)

8. Introduction to Deep Learning

Deep learning introduction
Deep learning prerequisites
History of artificial neural networks
Playing with TensorFlow
Training neural networks
Tuning neural networks
Introduction to TensorFlow
Handwriting recognition with TensorFlow, part 1
Handwriting recognition with TensorFlow, part 2
Introduction to Keras
Handwriting recognition with Keras
Classifier patterns with Keras
Predict political parties of politicians with Keras
Intro to convolutional neural networks (CNNs)
CNN architectures
Handwriting recognition with CNNs
Intro to recurrent neural networks (RNNs)
Training recurrent neural networks
Sentiment analysis of movie reviews using RNNs and Keras

9. Deep Learning for Recommender Systems

Intro to deep learning for recommenders
Restricted Boltzmann machines (RBMs)
Recommendations with RBMs, part 1
Recommendations with RBMs, part 2
Evaluating the RBM recommender
Tuning restricted Boltzmann machines
Exercise results: Tuning a RBM recommender
Auto-encoders for recommendations: Deep learning for recs
Recommendations with deep neural networks
Clickstream recommendations with RNNs
Get GRU4Rec working on your desktop
Exercise results: GRU4Rec in action
Bleeding edge alert: Deep factorization machines
More emerging tech to watch

10. Scaling It Up

Introduction and installation of Apache Spark
Apache Spark architecture
Movie recommendations with Spark, matrix factorization, and ALS
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

11. Real-World Challenges of Recommender Systems