Tonyloyt/Recommender

Course Material for Recommendation Engines

Recommender System and Deep Learning in Python

: repo for the course material of Recommender System and Deep Learning on Udemy

This course provides the in-depth course on recommendation systems in python. It consists of the Ranking Systems with non-personalization, Collaborative Filtering from scratch, Matrix Factorization and Deep Learning.

1. Basic Ranking systems

• How Hacker News ranking really works: scoring, controversy, and penalties

• The Evolution Of Hacker News

• Reddit sorting code

• Revealed: US spy operation that manipulates social media

• 5G Got me Fired

• Learning to rank

• How Not To Sort By Average Rating

• Wilson score interval

• reddit’s new comment sorting system

• Markov Chains Explained Visually

2. Collaborative filtering

• Course Note code from scratch

• An algorithmic framework for performing collaborative filtering

• Item-based collaborative filtering recommendation algorithms

3. Matrix Factorization

• Course Note code from scratch and faster one with numpy. Matrix Factorization with Keras and Spark

• FunkSVD

• Probabilistic Matrix Factorization

• Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo

• Algorithms for Non-negative Matrix Factorization

• Learning the parts of objects by non-negative matrix factorization

• AutoRec: Autoencoders Meet Collaborative Filtering

• Collaborative Filtering for Implicit Feedback Datasets

4. Neural Networks

• Course Note code Deep neural networks, ResNet structure and AutoRec

5. Restricted Boltzmann Machines

• Course Note code from scratch

• Restricted Boltzmann Machines for Collaborative Filtering