Recommender Systems and Deep Learning in Python
What you'll learn
- Understand and implement accurate recommendations for your users using simple and state-of-the-art algorithms
- Big data matrix factorization on Spark with an AWS EC2 cluster
- Matrix factorization / SVD in pure Numpy
- Matrix factorization in Keras
- Deep neural networks, residual networks, and autoencoder in Keras
- Restricted Boltzmann Machine in Tensorflow
- For earlier sections, just know some basic arithmetic
- For advanced sections, know calculus, linear algebra, and probability for a deeper understanding
- Be proficient in Python and the Numpy stack (see my free course)
- For the deep learning section, know the basics of using Keras
Believe it or not, almost all online businesses today make use of recommender systems in some way or another.
What do I mean by “recommender systems”, and why are they useful?
Let’s look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook.
Recommender systems form the very foundation of these technologies.
Google: Search results
They are why Google is the most successful technology company today.
YouTube: Video dashboard
I’m sure I’m not the only one who’s accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that?
That’s right. Recommender systems!
Facebook: So powerful that world governments are worried that the newsfeed has too much influence on people! (Or maybe they are worried about losing their own power... hmm...)
This course is a big bag of tricks that make recommender systems work across multiple platforms.
We’ll look at popular news feed algorithms, like Reddit, Hacker News, and Google PageRank.
We’ll look at Bayesian recommendation techniques that are being used by a large number of media companies today.
But this course isn’t just about news feeds.
Companies like Amazon, Netflix, and Spotify have been using recommendations to suggest products, movies, and music to customers for many years now.
These algorithms have led to billions of dollars in added revenue.
So I assure you, what you’re about to learn in this course is very real, very applicable, and will have a huge impact on your business.
For those of you who like to dig deep into the theory to understand how things really work, you know this is my specialty and there will be no shortage of that in this course. We’ll be covering state of the art algorithms like matrix factorization and deep learning (making use of both supervised and unsupervised learning - Autoencoders and Restricted Boltzmann Machines), and you’ll learn a bag full of tricks to improve upon baseline results.
As a bonus, we will also look how to perform matrix factorization using big data in Spark. We will create a cluster using Amazon EC2 instances with Amazon Web Services (AWS). Most other courses and tutorials look at the MovieLens 100k dataset - that is puny! Our examples make use of MovieLens 20 million.
Whether you sell products in your e-commerce store, or you simply write a blog - you can use these techniques to show the right recommendations to your users at the right time.
If you’re an employee at a company, you can use these techniques to impress your manager and get a raise!
I’ll see you in class!
This course is not "officially" part of my deep learning series. It contains a strong deep learning component, but there are many concepts in the course that are totally unrelated to deep learning.
"If you can't implement it, you don't understand it"
Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
For earlier sections, just know some basic arithmetic
For advanced sections, know calculus, linear algebra, and probability for a deeper understanding
Be proficient in Python and the Numpy stack (see my free course)
For the deep learning section, know the basics of using Keras
For the RBM section, know Tensorflow
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Who this course is for:
- Anyone who owns or operates an Internet business
- Students in machine learning, deep learning, artificial intelligence, and data science
- Professionals in machine learning, deep learning, artificial intelligence, and data science
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.
I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering.
Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.
I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.
My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.