[UDEMY] Recommender Systems and Deep Learning in Python - [FTU]

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The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques

Created by : Lazy Programmer Inc.
Last updated : 12/2018
Language : English
Caption (CC) : Included
BestSeller : 4.7 (198 ratings)
Torrent Contains : 170 Files, 8 Folders
Course Source : https://www.udemy.com/recommender-systems/

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

Requirements

• 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

Description

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 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!

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 RBM section, 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!

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

• 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)

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


For More Udemy Free Courses >>> http://www.freetutorials.eu
For more Lynda and other Courses >>> https://www.freecoursesonline.me/
Our Forum for discussion >>> https://discuss.freetutorials.eu/



Files:

[FreeTutorials.Eu] [UDEMY] Recommender Systems and Deep Learning in Python - [FTU] 1. Welcome
  • 1. Introduction.mp4 (21.6 MB)
  • 1. Introduction.vtt (3.8 KB)
  • 2. Outline of the course.mp4 (34.2 MB)
  • 2. Outline of the course.vtt (5.8 KB)
  • 3. Where to get the code.mp4 (27.1 MB)
  • 3. Where to get the code.vtt (6.1 KB)
2. Simple Recommendation Systems
  • 10. Bayesian Approach part 2 (Sampling and Ranking).mp4 (24.5 MB)
  • 10. Bayesian Approach part 2 (Sampling and Ranking).vtt (6.4 KB)
  • 11. Bayesian Approach part 3 (Gaussian).mp4 (32.7 MB)
  • 11. Bayesian Approach part 3 (Gaussian).vtt (9.0 KB)
  • 12. Bayesian Approach part 4 (Code).mp4 (106.5 MB)
  • 12. Bayesian Approach part 4 (Code).vtt (12.2 KB)
  • 13. Demographics and Supervised Learning.mp4 (48.6 MB)
  • 13. Demographics and Supervised Learning.vtt (7.9 KB)
  • 14. PageRank (part 1).mp4 (54.9 MB)
  • 14. PageRank (part 1).vtt (11.4 KB)
  • 15. PageRank (part 2).mp4 (49.9 MB)
  • 15. PageRank (part 2).vtt (12.8 KB)
  • 16. Evaluating a Ranking.mp4 (34.9 MB)
  • 16. Evaluating a Ranking.vtt (5.2 KB)
  • 17. Section Conclusion.mp4 (31.0 MB)
  • 17. Section Conclusion.vtt (4.4 KB)
  • 1. Section Introduction and Outline.mp4 (27.2 MB)
  • 1. Section Introduction and Outline.vtt (4.8 KB)
  • 2. Perspective for this Section.mp4 (18.3 MB)
  • 2. Perspective for this Section.vtt (4.5 KB)
  • 3. Basic Intuitions.mp4 (30.6 MB)
  • 3. Basic Intuitions.vtt (5.9 KB)
  • 4. Associations.mp4 (29.9 MB)
  • 4. Associations.vtt (5.0 KB)
  • 5. Hacker News - Will you be penalized for talking about the NSA.mp4 (37.6 MB)
  • 5. Hacker News - Will you be penalized for talking about the NSA.vtt (8.0 KB)
  • 6. Reddit - Should censorship based on politics be allowed.mp4 (53.1 MB)
  • 6. Reddit - Should censorship based on politics be allowed.vtt (10.1 KB)
  • 7. Problems with Average Rating _ Explore vs. Exploit (part 1).mp4 (47.7 MB)
  • 7. Problems with Average Rating _ Explore vs. Exploit (part 1).vtt (12.3 KB)
  • 8. Problems with Average Rating _ Explore vs. Exploit (part 2).mp4 (45.6 MB)
  • 8. Problems with Average Rating _ Explore vs. Exploit (part 2).vtt (9.2 KB)
  • 9. Bayesian Approach part 1 (Optional).mp4 (44.9 MB)
  • 9. Bayesian Approach part 1 (Optional).vtt (0.0 KB)
3. Collaborative Filtering
  • 1. Collaborative Filtering Section Introduction.mp4 (51.6 MB)
  • 1. Collaborative Filtering Section Introduction.vtt (13.0 KB)
  • 2. User-User Collaborative Filtering.mp4 (60.7 MB)
  • 2. User-User Collaborative Filtering.vtt (15.8 KB)
  • 3. Collaborative Filtering Exercise Prep.mp4 (43.6 MB)
  • 3. Collaborative Filtering Exercise Prep.vtt (12.0 KB)
  • 4. Data Preprocessing.mp4 (115.9 MB)
  • 4. Data Preprocessing.vtt (17.5 KB)
  • 5. User-User Collaborative Filtering in Code.mp4 (153.6 MB)
  • 5. User-User Collaborative Filtering in Code.vtt (18.6 KB)
  • 6. Item-Item Collaborative Filtering.mp4 (47.6 MB)
  • 6. Item-Item Collaborative Filtering.vtt (10.1 KB)
  • 7. Item-Item Collaborative Filtering in Code.mp4 (69.5 MB)
  • 7. Item-Item Collaborative Filtering in Code.vtt (7.6 KB)
  • 8. Collaborative Filtering Section Conclusion.mp4 (29.3 MB)
  • 8. Collaborative Filtering Section Conclusion.vtt (6.2 KB)
4. Matrix Factorization and Deep Learning
  • 10. Probabilistic Matrix Factorization.mp4 (23.0 MB)
  • 10. Probabilistic Matrix Factorization.vtt (6.6 KB)
  • 11. Bayesian Matrix Factorization.mp4 (20.7 MB)
  • 11. Bayesian Matrix Factorization.vtt (5.9 KB)
  • 12. Matrix Factorization in Keras (Discussion).mp4 (32.2 MB)
  • 12. Matrix Factorization in Keras (Discussion).vtt (8.4 KB)
  • 13. Matrix Factorization in Keras (Code).mp4 (63.9 MB)
  • 13. Matrix Factorization in Keras (Code).vtt (8.0 KB)
  • 14. Deep Neural Network (Discussion).mp4 (15.0 MB)
  • 14. Deep Neural Network (Discussion).vtt (3.1 KB)
  • 15. Deep Neural Network (Code).mp4 (25.1 MB)
  • 15. Deep Neural Network (Code).vtt (2.9 KB)
  • 16. Residual Learning (Discussion).mp4 (7.5 MB)
  • 16. Residual Learning (Discussion).vtt (2.2 KB)
  • 17. Residual Learning (Code).mp4 (17.2 MB)
  • 17. Residual Learning (Code).vtt (1.7 KB)
  • 18. Autoencoders (AutoRec) Discussion.mp4 (48.9 MB)
  • 18. Autoencoders (AutoRec) Discussion.vtt (11.5 KB)
  • 19. Autoencoders (AutoRec) Code.mp4 (102.3 MB)
  • 19. Autoencoders (AutoRec) Code.vtt (12.6 KB)
  • 1. Matrix Factorization Section Introduction.mp4 (16.9 MB)
  • 1. Matrix Factorization Section Introduction.vtt (4.9 KB)
  • 2. Matrix Factorization - First Steps.mp4 (68.7 MB)
  • 2. Matrix Factorization - First Steps.vtt (16.7 KB)
  • 3. Matrix Factorization - Training.mp4 (32.6 MB)
  • 3. Matrix Factorization - Training.vtt (9.7 KB)
  • 4. Matrix Factorization - Expanding Our Model.mp4 (33.7 MB)
  • 4. Matrix Factorization - Expanding Our Model.vtt (8.4 KB)
  • 5. Matrix Factorization - Regularization.mp4 (22.4 MB)
  • 5. Matrix Factorization - Regularization.vtt (6.4 KB)
  • 6. Matrix Factorization - Exercise Prompt.mp4 (4.3 MB)
  • 6. Matrix Factorization - Exercise Prompt.vtt (1.3 KB)
  • 7. Matrix Factorization in Code.mp4 (52.4 MB)
  • 7. Matrix Factorization in Code.vtt (6.4 KB)
  • 8. Matrix Factorization in Code - Vectorized.mp4 (97.4 MB)
  • 8. Matrix Factorization in Code - Vectorized.vtt (11.1 KB)
  • 9. SVD (Singular Value Decomposition).mp4 (32.6 MB)
  • 9. SVD (Singular Value Decomposition).vtt (8.2 KB)
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering
  • 10. RBM Code pt 1.mp4 (70.4 MB)
  • 10. RBM Code pt 1.vtt (8.7 KB)
  • 11. RBM Code pt 2.mp4 (39.6 MB)
  • 11. RBM Code pt 2.vtt (4.6 KB)
  • 12. RBM Code pt 3.mp4 (128.5 MB)
  • 12. RBM Code pt 3.vtt (12.0 KB)
  • 13. Speeding up the RBM Code.mp4 (82.9 MB)
  • 13. Speeding up the RBM Code.vtt (7.9 KB)
  • 1. RBMs for Collaborative Filtering Section Introduction.mp4 (10.3 MB)
  • 1. RBMs for Collaborative Filtering Section Introduction.vtt (2.4 KB)
  • 2. Intro to RBMs.mp4 (39.4 MB)
  • 2. Intro to RBMs.vtt (8.9 KB)
  • 3. Motivation Behind RBMs.mp4 (34.0 MB)
  • 3. Motivation Behind RBMs.vtt (7.5 KB)
  • 4. Intractability.mp4 (12.9 MB)
  • 4. Intractability.vtt (3.3 KB)
  • 5. Neural Network Equations.mp4 (31.7 MB)
  • 5. Neural Network Equations.vtt (7.4 KB)
  • 6. Training an RBM (part 1).mp4 (49.1 MB)
  • 6. Training an RBM (part 1).vtt (11.8 KB)
  • 7. Training an RBM (part 2).mp4 (27.3 MB)
  • 7. Training an RBM (part 2).vtt (6.4 KB)
  • 8. Training an RBM (part 3) - Free Energy.mp4 (27.6 MB)
  • 8. Training an RBM (part 3) - Free Energy.vtt (7.0 KB)
  • 9. Categorical RBM for Recommender System Ratings.mp4 (47.6 MB)
  • 9. Categorical RBM for Recommender System Ratings.vtt (12.0 KB)
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2
  • 1. Big Data and Spark Section Introduction.mp4 (35.5 MB)
  • 1. Big Data and Spark Section Introduction.vtt (8.2 KB)
  • 2. Setting up Spark in your Local Environment.mp4 (34.7 MB)
  • 2. Setting up Spark in your Local Environment.vtt (8.6 KB)
  • 3. Matrix Factorization in Spark.mp4 (110.0 MB)
  • 3. Matrix Factorization in Spark.vtt (9.7 KB)
  • 4. Spark Submit.mp4 (56.1 MB)
  • 4. Spark Submit.vtt (7.0 KB)
  • 5. Setting up a Spark Cluster on AWS EC2.mp4 (73.2 MB)
  • 5. Setting up a Spark Cluster on AWS EC2.vtt (13.8 KB)
  • 6. Making Predictions in the Real World.mp4 (11.3 MB)
  • 6. Making Predictions in the Real World.vtt (3.0 KB)
7. Basics Review
  • 1. Keras Discussion.mp4 (27.6 MB)
  • 1. Keras Discussion.vtt (8.0 KB)
  • 2. Keras Neural Network in Code.mp4 (66.2 MB)
  • 2. Keras Neural Network in Code.vtt (6.5 KB)
  • 3. Keras Functional API.mp4 (38.6 MB)
  • 3. Keras Functional API.vtt (4.7 KB)
  • 4. Confidence Intervals (Appendix).mp4 (39.8 MB)
  • 4. Confidence Intervals (Appendix).vtt (11.5 KB)
  • 5. Gaussian Conjugate Prior (Appendix).mp4 (25.1 MB)
  • 5. Gaussian Conjugate Prior (Appendix).vtt (6.3 KB)
8. Appendix
  • 10. Python 2 vs Python 3.mp4 (19.1 MB)
  • 10. Python 2 vs Python 3.vtt (5.3 KB)
  • 11. BONUS Where to get discount coupons and FREE deep learning material.mp4 (13.4 MB)
  • 11. BONUS Where to get discount coupons and FREE deep learning material.vtt (3.0 KB)
  • 1. What is the Appendix.mp4 (18.1 MB)
  • 1. What is the Appendix.vtt (3.3 KB)
  • 2. Windows-Focused Environment Setup 2018.mp4 (194.3 MB)
  • 2. Windows-Focused Environment Setup 2018.vtt (17.4 KB)
  • 3. How to How to install Numpy, Theano, Tensorflow, etc....mp4 (167.0 MB)
  • 3. How to How to install Numpy, Theano, Tensorflow, etc....vtt (12.4 KB)
  • 4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 (117.3 MB)
  • 4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt (28.0 KB)
  • 5. How to Succeed in this Course (Long Version).mp4 (39.2 MB)
  • 5. How to Succeed in this Course (Long Version).vtt (12.8 KB)
  • 6. How to Code by Yourself (part 1).mp4 (82.5 MB)
  • 6. How to Code by Yourself (part 1).vtt (19.6 KB)
  • 7. How to Code by Yourself (part 2).mp4 (56.7 MB)
  • 7. How to Code by Yourself (part 2).vtt (11.6 KB)
  • 8. What order should I take your courses in (part 1).mp4 (88.4 MB)
  • 8. What order should I take your courses in (part 1).vtt (14.2 KB)
  • 9. What order should I take your courses in (part 2).mp4 (123.0 MB)
  • 9. What order should I take your courses in (part 2).vtt (20.3 KB)
  • Discuss.FreeTutorials.Us.html (165.7 KB)
  • FreeCoursesOnline.Me.html (108.3 KB)
  • FreeTutorials.Eu.html (102.2 KB)
  • How you can help Team-FTU.txt (0.3 KB)
  • [TGx]Downloaded from torrentgalaxy.org.txt (0.5 KB)
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