Recommender Systems and Deep Learning in Python

Recommender Systems and Deep Learning in Python, available at $109.99, has an average rating of 4.7, with 94 lectures, based on 5605 reviews, and has 30023 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who owns or operates an Internet business or Students in machine learning, deep learning, artificial intelligence, and data science or Professionals in machine learning, deep learning, artificial intelligence, and data science It is particularly useful for Anyone who owns or operates an Internet business or Students in machine learning, deep learning, artificial intelligence, and data science or Professionals in machine learning, deep learning, artificial intelligence, and data science.

Enroll now: Recommender Systems and Deep Learning in Python

Summary

Title: Recommender Systems and Deep Learning in Python

Price: $109.99

Average Rating: 4.7

Number of Lectures: 94

Number of Published Lectures: 93

Number of Curriculum Items: 94

Number of Published Curriculum Objects: 93

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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…)

Amazing!

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 supervisedand 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!

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.

“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…

Suggested Prerequisites:

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)

UNIQUE FEATURES

Every line of code explained in detail – email me any time if you disagree
No wasted time “typing” on the keyboard like other courses – let’s be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math – get important details about algorithms that other courses leave out

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction

Lecture 2: Outline of the course

Lecture 3: Where to get the code

Lecture 4: How to Succeed in this Course

Chapter 2: Simple Recommendation Systems

Lecture 1: Section Introduction and Outline

Lecture 2: Perspective for this Section

Lecture 3: Basic Intuitions

Lecture 4: Associations

Lecture 5: Hacker News – Will you be penalized for talking about the NSA?

Lecture 6: Reddit – Should censorship based on politics be allowed?

Lecture 7: Problems with Average Rating & Explore vs. Exploit (part 1)

Lecture 8: Problems with Average Rating & Explore vs. Exploit (part 2)

Lecture 9: Bayesian Ranking (Beginner Version)

Lecture 10: Demographics and Supervised Learning

Lecture 11: PageRank (part 1)

Lecture 12: PageRank (part 2)

Lecture 13: Evaluating a Ranking

Lecture 14: Section Conclusion

Lecture 15: Suggestion Box

Chapter 3: Collaborative Filtering

Lecture 1: Collaborative Filtering Section Introduction

Lecture 2: User-User Collaborative Filtering

Lecture 3: Collaborative Filtering Exercise Prep

Lecture 4: Data Preprocessing

Lecture 5: User-User Collaborative Filtering in Code

Lecture 6: Item-Item Collaborative Filtering

Lecture 7: Item-Item Collaborative Filtering in Code

Lecture 8: Collaborative Filtering Section Conclusion

Chapter 4: Beginner Q&A

Lecture 1: How do I Choose Which Model to Use?

Lecture 2: How do I Solve the Cold-Start Problem?

Lecture 3: What if I Don't Like Math or Programming?

Chapter 5: Matrix Factorization and Deep Learning

Lecture 1: Matrix Factorization Section Introduction

Lecture 2: Matrix Factorization – First Steps

Lecture 3: Matrix Factorization – Training

Lecture 4: Matrix Factorization – Expanding Our Model

Lecture 5: Matrix Factorization – Regularization

Lecture 6: Matrix Factorization – Exercise Prompt

Lecture 7: Matrix Factorization in Code

Lecture 8: Matrix Factorization in Code – Vectorized

Lecture 9: SVD (Singular Value Decomposition)

Lecture 10: Probabilistic Matrix Factorization

Lecture 11: Bayesian Matrix Factorization

Lecture 12: Matrix Factorization in Keras (Discussion)

Lecture 13: Matrix Factorization in Keras (Code)

Lecture 14: Deep Neural Network (Discussion)

Lecture 15: Deep Neural Network (Code)

Lecture 16: Residual Learning (Discussion)

Lecture 17: Residual Learning (Code)

Lecture 18: Autoencoders (AutoRec) Discussion

Lecture 19: Autoencoders (AutoRec) Code

Chapter 6: Restricted Boltzmann Machines (RBMs) for Collaborative Filtering

Lecture 1: RBMs for Collaborative Filtering Section Introduction

Lecture 2: Intro to RBMs

Lecture 3: Motivation Behind RBMs

Lecture 4: Intractability

Lecture 5: Neural Network Equations

Lecture 6: Training an RBM (part 1)

Lecture 7: Training an RBM (part 2)

Lecture 8: Training an RBM (part 3) – Free Energy

Lecture 9: Categorical RBM for Recommender System Ratings

Lecture 10: RBM Code pt 1

Lecture 11: RBM Code pt 2

Lecture 12: RBM Code pt 3

Lecture 13: Speeding up the RBM Code

Chapter 7: Big Data Matrix Factorization with Spark Cluster on AWS / EC2

Lecture 1: Big Data and Spark Section Introduction

Lecture 2: Setting up Spark in your Local Environment

Lecture 3: Matrix Factorization in Spark

Lecture 4: Spark Submit

Lecture 5: Setting up a Spark Cluster on AWS / EC2

Lecture 6: Making Predictions in the Real World

Chapter 8: Basics Review

Lecture 1: (Review) Keras Discussion

Lecture 2: (Review) Keras Neural Network in Code

Lecture 3: (Review) Keras Functional API

Lecture 4: (Review) How to easily convert Keras into Tensorflow 2.0 code

Lecture 5: (Review) Confidence Intervals

Lecture 6: (Review) Gaussian Conjugate Prior

Chapter 9: Bayesian Ranking (Scary Version)

Lecture 1: Bayesian Approach part 0 (Preparation)

Lecture 2: Bayesian Approach part 1 (Optional)

Lecture 3: Optional: Bayesian Approach part 2 (Sampling and Ranking)

Lecture 4: Optional: Bayesian Approach part 3 (Gaussian)

Lecture 5: Optional: Bayesian Approach part 4 (Code)

Lecture 6: Why don't we just use a library?

Chapter 10: Setting Up Your Environment (FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to How to install Numpy, Theano, Tensorflow, etc…

Chapter 11: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: How to Code by Yourself (part 1)

Lecture 2: How to Code by Yourself (part 2)

Lecture 3: Proof that using Jupyter Notebook is the same as not using it

Lecture 4: Python 2 vs Python 3

Chapter 12: Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 1: How to Succeed in this Course (Long Version)

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 52 votes
2 stars: 49 votes
3 stars: 220 votes
4 stars: 1908 votes
5 stars: 3376 votes

Frequently Asked Questions

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