Recommender Systems and Deep Learning in Python هذه الدورة تدرس باللغة الإنجليزية

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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?

Lets 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

Im sure Im not the only one whos accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that?

Thats 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...)

Amazing!

This course is a big bag of tricks that make recommender systems work across multiple platforms.

Well look at popular news feed algorithms, like Reddit, Hacker News, and Google PageRank.

Well look at Bayesian recommendation techniques that are being used by a large number of media companies today.

But this course isnt 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 youre 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. Well 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 youll 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 youre an employee at a company, you can use these techniques to impress your manager and get a raise!

Ill see you in class!

NOTE:

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.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

• 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 RBMsection, know Tensorflow

TIPS (for getting through the course):

• Watch it at 2x.

• Take handwritten notes. This will drastically increase your ability to retain the information.

• Write down the equations. If you don't, I guarantee it will just look like gibberish.

• Ask lots of questions on the discussion board. The more the better!

• The best exercises will take you days or weeks to complete.

• Write code yourself, don't just sit there and look at my code. This is not a philosophy course!

WHATORDERSHOULDITAKEYOURCOURSESIN?:

• Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)