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

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Description

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/

File list

[FreeTutorials.Eu] [UDEMY] Recommender Systems and Deep Learning in Python - [FTU]
1. Welcome/1. Introduction.mp4 21.6 MB
1. Welcome/1. Introduction.vtt 3.8 KB
1. Welcome/2. Outline of the course.mp4 34.2 MB
1. Welcome/2. Outline of the course.vtt 5.8 KB
1. Welcome/3. Where to get the code.mp4 27.1 MB
1. Welcome/3. Where to get the code.vtt 6.1 KB
2. Simple Recommendation Systems/1. Section Introduction and Outline.mp4 27.2 MB
2. Simple Recommendation Systems/1. Section Introduction and Outline.vtt 4.8 KB
2. Simple Recommendation Systems/10. Bayesian Approach part 2 (Sampling and Ranking).mp4 24.5 MB
2. Simple Recommendation Systems/10. Bayesian Approach part 2 (Sampling and Ranking).vtt 6.4 KB
2. Simple Recommendation Systems/11. Bayesian Approach part 3 (Gaussian).mp4 32.7 MB
2. Simple Recommendation Systems/11. Bayesian Approach part 3 (Gaussian).vtt 9 KB
2. Simple Recommendation Systems/12. Bayesian Approach part 4 (Code).mp4 106.5 MB
2. Simple Recommendation Systems/12. Bayesian Approach part 4 (Code).vtt 12.2 KB
2. Simple Recommendation Systems/13. Demographics and Supervised Learning.mp4 48.6 MB
2. Simple Recommendation Systems/13. Demographics and Supervised Learning.vtt 7.9 KB
2. Simple Recommendation Systems/14. PageRank (part 1).mp4 54.9 MB
2. Simple Recommendation Systems/14. PageRank (part 1).vtt 11.4 KB
2. Simple Recommendation Systems/15. PageRank (part 2).mp4 49.9 MB
2. Simple Recommendation Systems/15. PageRank (part 2).vtt 12.8 KB
2. Simple Recommendation Systems/16. Evaluating a Ranking.mp4 34.9 MB
2. Simple Recommendation Systems/16. Evaluating a Ranking.vtt 5.2 KB
2. Simple Recommendation Systems/17. Section Conclusion.mp4 31 MB
2. Simple Recommendation Systems/17. Section Conclusion.vtt 4.4 KB
2. Simple Recommendation Systems/2. Perspective for this Section.mp4 18.3 MB
2. Simple Recommendation Systems/2. Perspective for this Section.vtt 4.5 KB
2. Simple Recommendation Systems/3. Basic Intuitions.mp4 30.6 MB
2. Simple Recommendation Systems/3. Basic Intuitions.vtt 5.9 KB
2. Simple Recommendation Systems/4. Associations.mp4 29.9 MB
2. Simple Recommendation Systems/4. Associations.vtt 5 KB
2. Simple Recommendation Systems/5. Hacker News - Will you be penalized for talking about the NSA.mp4 37.6 MB
2. Simple Recommendation Systems/5. Hacker News - Will you be penalized for talking about the NSA.vtt 8 KB
2. Simple Recommendation Systems/6. Reddit - Should censorship based on politics be allowed.mp4 53.1 MB
2. Simple Recommendation Systems/6. Reddit - Should censorship based on politics be allowed.vtt 10.1 KB
2. Simple Recommendation Systems/7. Problems with Average Rating _ Explore vs. Exploit (part 1).mp4 47.7 MB
2. Simple Recommendation Systems/7. Problems with Average Rating _ Explore vs. Exploit (part 1).vtt 12.3 KB
2. Simple Recommendation Systems/8. Problems with Average Rating _ Explore vs. Exploit (part 2).mp4 45.6 MB
2. Simple Recommendation Systems/8. Problems with Average Rating _ Explore vs. Exploit (part 2).vtt 9.2 KB
2. Simple Recommendation Systems/9. Bayesian Approach part 1 (Optional).mp4 44.9 MB
2. Simple Recommendation Systems/9. Bayesian Approach part 1 (Optional).vtt 0 B
3. Collaborative Filtering/1. Collaborative Filtering Section Introduction.mp4 51.6 MB
3. Collaborative Filtering/1. Collaborative Filtering Section Introduction.vtt 13 KB
3. Collaborative Filtering/2. User-User Collaborative Filtering.mp4 60.7 MB
3. Collaborative Filtering/2. User-User Collaborative Filtering.vtt 15.8 KB
3. Collaborative Filtering/3. Collaborative Filtering Exercise Prep.mp4 43.6 MB
3. Collaborative Filtering/3. Collaborative Filtering Exercise Prep.vtt 12 KB
3. Collaborative Filtering/4. Data Preprocessing.mp4 115.9 MB
3. Collaborative Filtering/4. Data Preprocessing.vtt 17.5 KB
3. Collaborative Filtering/5. User-User Collaborative Filtering in Code.mp4 153.6 MB
3. Collaborative Filtering/5. User-User Collaborative Filtering in Code.vtt 18.6 KB
3. Collaborative Filtering/6. Item-Item Collaborative Filtering.mp4 47.6 MB
3. Collaborative Filtering/6. Item-Item Collaborative Filtering.vtt 10.1 KB
3. Collaborative Filtering/7. Item-Item Collaborative Filtering in Code.mp4 69.5 MB
3. Collaborative Filtering/7. Item-Item Collaborative Filtering in Code.vtt 7.6 KB
3. Collaborative Filtering/8. Collaborative Filtering Section Conclusion.mp4 29.3 MB
3. Collaborative Filtering/8. Collaborative Filtering Section Conclusion.vtt 6.2 KB
4. Matrix Factorization and Deep Learning/1. Matrix Factorization Section Introduction.mp4 16.9 MB
4. Matrix Factorization and Deep Learning/1. Matrix Factorization Section Introduction.vtt 4.9 KB
4. Matrix Factorization and Deep Learning/10. Probabilistic Matrix Factorization.mp4 23 MB
4. Matrix Factorization and Deep Learning/10. Probabilistic Matrix Factorization.vtt 6.6 KB
4. Matrix Factorization and Deep Learning/11. Bayesian Matrix Factorization.mp4 20.7 MB
4. Matrix Factorization and Deep Learning/11. Bayesian Matrix Factorization.vtt 5.9 KB
4. Matrix Factorization and Deep Learning/12. Matrix Factorization in Keras (Discussion).mp4 32.2 MB
4. Matrix Factorization and Deep Learning/12. Matrix Factorization in Keras (Discussion).vtt 8.4 KB
4. Matrix Factorization and Deep Learning/13. Matrix Factorization in Keras (Code).mp4 63.9 MB
4. Matrix Factorization and Deep Learning/13. Matrix Factorization in Keras (Code).vtt 8 KB
4. Matrix Factorization and Deep Learning/14. Deep Neural Network (Discussion).mp4 15 MB
4. Matrix Factorization and Deep Learning/14. Deep Neural Network (Discussion).vtt 3.1 KB
4. Matrix Factorization and Deep Learning/15. Deep Neural Network (Code).mp4 25.1 MB
4. Matrix Factorization and Deep Learning/15. Deep Neural Network (Code).vtt 2.9 KB
4. Matrix Factorization and Deep Learning/16. Residual Learning (Discussion).mp4 7.5 MB
4. Matrix Factorization and Deep Learning/16. Residual Learning (Discussion).vtt 2.2 KB
4. Matrix Factorization and Deep Learning/17. Residual Learning (Code).mp4 17.2 MB
4. Matrix Factorization and Deep Learning/17. Residual Learning (Code).vtt 1.7 KB
4. Matrix Factorization and Deep Learning/18. Autoencoders (AutoRec) Discussion.mp4 48.9 MB
4. Matrix Factorization and Deep Learning/18. Autoencoders (AutoRec) Discussion.vtt 11.5 KB
4. Matrix Factorization and Deep Learning/19. Autoencoders (AutoRec) Code.mp4 102.3 MB
4. Matrix Factorization and Deep Learning/19. Autoencoders (AutoRec) Code.vtt 12.6 KB
4. Matrix Factorization and Deep Learning/2. Matrix Factorization - First Steps.mp4 68.7 MB
4. Matrix Factorization and Deep Learning/2. Matrix Factorization - First Steps.vtt 16.7 KB
4. Matrix Factorization and Deep Learning/3. Matrix Factorization - Training.mp4 32.6 MB
4. Matrix Factorization and Deep Learning/3. Matrix Factorization - Training.vtt 9.7 KB
4. Matrix Factorization and Deep Learning/4. Matrix Factorization - Expanding Our Model.mp4 33.7 MB
4. Matrix Factorization and Deep Learning/4. Matrix Factorization - Expanding Our Model.vtt 8.4 KB
4. Matrix Factorization and Deep Learning/5. Matrix Factorization - Regularization.mp4 22.4 MB
4. Matrix Factorization and Deep Learning/5. Matrix Factorization - Regularization.vtt 6.4 KB
4. Matrix Factorization and Deep Learning/6. Matrix Factorization - Exercise Prompt.mp4 4.3 MB
4. Matrix Factorization and Deep Learning/6. Matrix Factorization - Exercise Prompt.vtt 1.3 KB
4. Matrix Factorization and Deep Learning/7. Matrix Factorization in Code.mp4 52.4 MB
4. Matrix Factorization and Deep Learning/7. Matrix Factorization in Code.vtt 6.4 KB
4. Matrix Factorization and Deep Learning/8. Matrix Factorization in Code - Vectorized.mp4 97.4 MB
4. Matrix Factorization and Deep Learning/8. Matrix Factorization in Code - Vectorized.vtt 11.1 KB
4. Matrix Factorization and Deep Learning/9. SVD (Singular Value Decomposition).mp4 32.6 MB
4. Matrix Factorization and Deep Learning/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
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/10. RBM Code pt 1.vtt 8.7 KB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/11. RBM Code pt 2.mp4 39.6 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/11. RBM Code pt 2.vtt 4.6 KB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/12. RBM Code pt 3.mp4 128.5 MB

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