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

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4.03 GB

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1e9426569178176e4474f566414a93061df681bf

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03-01-2019 17:15

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File	Size
1. Welcome/1. Introduction.mp4	21.63 MB
1. Welcome/1. Introduction.vtt	3.84 kB
1. Welcome/2. Outline of the course.mp4	34.19 MB
1. Welcome/2. Outline of the course.vtt	5.83 kB
1. Welcome/3. Where to get the code.mp4	27.14 MB
1. Welcome/3. Where to get the code.vtt	6.05 kB
2. Simple Recommendation Systems/1. Section Introduction and Outline.mp4	27.25 MB
2. Simple Recommendation Systems/1. Section Introduction and Outline.vtt	4.76 kB
2. Simple Recommendation Systems/10. Bayesian Approach part 2 (Sampling and Ranking).mp4	24.50 MB
2. Simple Recommendation Systems/10. Bayesian Approach part 2 (Sampling and Ranking).vtt	6.44 kB
2. Simple Recommendation Systems/11. Bayesian Approach part 3 (Gaussian).mp4	32.72 MB
2. Simple Recommendation Systems/11. Bayesian Approach part 3 (Gaussian).vtt	8.96 kB
2. Simple Recommendation Systems/12. Bayesian Approach part 4 (Code).mp4	106.51 MB
2. Simple Recommendation Systems/12. Bayesian Approach part 4 (Code).vtt	12.21 kB
2. Simple Recommendation Systems/13. Demographics and Supervised Learning.mp4	48.57 MB
2. Simple Recommendation Systems/13. Demographics and Supervised Learning.vtt	7.89 kB
2. Simple Recommendation Systems/14. PageRank (part 1).mp4	54.89 MB
2. Simple Recommendation Systems/14. PageRank (part 1).vtt	11.36 kB
2. Simple Recommendation Systems/15. PageRank (part 2).mp4	49.92 MB
2. Simple Recommendation Systems/15. PageRank (part 2).vtt	12.79 kB
2. Simple Recommendation Systems/16. Evaluating a Ranking.mp4	34.93 MB
2. Simple Recommendation Systems/16. Evaluating a Ranking.vtt	5.22 kB
2. Simple Recommendation Systems/17. Section Conclusion.mp4	31.01 MB
2. Simple Recommendation Systems/17. Section Conclusion.vtt	4.39 kB
2. Simple Recommendation Systems/2. Perspective for this Section.mp4	18.29 MB
2. Simple Recommendation Systems/2. Perspective for this Section.vtt	4.51 kB
2. Simple Recommendation Systems/3. Basic Intuitions.mp4	30.62 MB
2. Simple Recommendation Systems/3. Basic Intuitions.vtt	5.92 kB
2. Simple Recommendation Systems/4. Associations.mp4	29.89 MB
2. Simple Recommendation Systems/4. Associations.vtt	5.02 kB
2. Simple Recommendation Systems/5. Hacker News - Will you be penalized for talking about the NSA.mp4	37.63 MB
2. Simple Recommendation Systems/5. Hacker News - Will you be penalized for talking about the NSA.vtt	8.05 kB
2. Simple Recommendation Systems/6. Reddit - Should censorship based on politics be allowed.mp4	53.07 MB
2. Simple Recommendation Systems/6. Reddit - Should censorship based on politics be allowed.vtt	10.14 kB
2. Simple Recommendation Systems/7. Problems with Average Rating _ Explore vs. Exploit (part 1).mp4	47.72 MB
2. Simple Recommendation Systems/7. Problems with Average Rating _ Explore vs. Exploit (part 1).vtt	12.30 kB
2. Simple Recommendation Systems/8. Problems with Average Rating _ Explore vs. Exploit (part 2).mp4	45.62 MB
2. Simple Recommendation Systems/8. Problems with Average Rating _ Explore vs. Exploit (part 2).vtt	9.22 kB
2. Simple Recommendation Systems/9. Bayesian Approach part 1 (Optional).mp4	44.95 MB
2. Simple Recommendation Systems/9. Bayesian Approach part 1 (Optional).vtt	0.00 B
3. Collaborative Filtering/1. Collaborative Filtering Section Introduction.mp4	51.63 MB
3. Collaborative Filtering/1. Collaborative Filtering Section Introduction.vtt	13.02 kB
3. Collaborative Filtering/2. User-User Collaborative Filtering.mp4	60.69 MB
3. Collaborative Filtering/2. User-User Collaborative Filtering.vtt	15.77 kB
3. Collaborative Filtering/3. Collaborative Filtering Exercise Prep.mp4	43.58 MB
3. Collaborative Filtering/3. Collaborative Filtering Exercise Prep.vtt	12.05 kB
3. Collaborative Filtering/4. Data Preprocessing.mp4	115.91 MB
3. Collaborative Filtering/4. Data Preprocessing.vtt	17.51 kB
3. Collaborative Filtering/5. User-User Collaborative Filtering in Code.mp4	153.58 MB
3. Collaborative Filtering/5. User-User Collaborative Filtering in Code.vtt	18.55 kB
3. Collaborative Filtering/6. Item-Item Collaborative Filtering.mp4	47.58 MB
3. Collaborative Filtering/6. Item-Item Collaborative Filtering.vtt	10.09 kB
3. Collaborative Filtering/7. Item-Item Collaborative Filtering in Code.mp4	69.46 MB
3. Collaborative Filtering/7. Item-Item Collaborative Filtering in Code.vtt	7.56 kB
3. Collaborative Filtering/8. Collaborative Filtering Section Conclusion.mp4	29.35 MB
3. Collaborative Filtering/8. Collaborative Filtering Section Conclusion.vtt	6.22 kB
4. Matrix Factorization and Deep Learning/1. Matrix Factorization Section Introduction.mp4	16.87 MB
4. Matrix Factorization and Deep Learning/1. Matrix Factorization Section Introduction.vtt	4.94 kB
4. Matrix Factorization and Deep Learning/10. Probabilistic Matrix Factorization.mp4	22.96 MB
4. Matrix Factorization and Deep Learning/10. Probabilistic Matrix Factorization.vtt	6.63 kB
4. Matrix Factorization and Deep Learning/11. Bayesian Matrix Factorization.mp4	20.72 MB
4. Matrix Factorization and Deep Learning/11. Bayesian Matrix Factorization.vtt	5.89 kB
4. Matrix Factorization and Deep Learning/12. Matrix Factorization in Keras (Discussion).mp4	32.24 MB
4. Matrix Factorization and Deep Learning/12. Matrix Factorization in Keras (Discussion).vtt	8.43 kB
4. Matrix Factorization and Deep Learning/13. Matrix Factorization in Keras (Code).mp4	63.94 MB
4. Matrix Factorization and Deep Learning/13. Matrix Factorization in Keras (Code).vtt	8.01 kB
4. Matrix Factorization and Deep Learning/14. Deep Neural Network (Discussion).mp4	14.98 MB
4. Matrix Factorization and Deep Learning/14. Deep Neural Network (Discussion).vtt	3.08 kB
4. Matrix Factorization and Deep Learning/15. Deep Neural Network (Code).mp4	25.10 MB
4. Matrix Factorization and Deep Learning/15. Deep Neural Network (Code).vtt	2.90 kB
4. Matrix Factorization and Deep Learning/16. Residual Learning (Discussion).mp4	7.54 MB
4. Matrix Factorization and Deep Learning/16. Residual Learning (Discussion).vtt	2.18 kB
4. Matrix Factorization and Deep Learning/17. Residual Learning (Code).mp4	17.22 MB
4. Matrix Factorization and Deep Learning/17. Residual Learning (Code).vtt	1.72 kB
4. Matrix Factorization and Deep Learning/18. Autoencoders (AutoRec) Discussion.mp4	48.90 MB
4. Matrix Factorization and Deep Learning/18. Autoencoders (AutoRec) Discussion.vtt	11.54 kB
4. Matrix Factorization and Deep Learning/19. Autoencoders (AutoRec) Code.mp4	102.31 MB
4. Matrix Factorization and Deep Learning/19. Autoencoders (AutoRec) Code.vtt	12.62 kB
4. Matrix Factorization and Deep Learning/2. Matrix Factorization - First Steps.mp4	68.68 MB
4. Matrix Factorization and Deep Learning/2. Matrix Factorization - First Steps.vtt	16.66 kB
4. Matrix Factorization and Deep Learning/3. Matrix Factorization - Training.mp4	32.59 MB
4. Matrix Factorization and Deep Learning/3. Matrix Factorization - Training.vtt	9.67 kB
4. Matrix Factorization and Deep Learning/4. Matrix Factorization - Expanding Our Model.mp4	33.72 MB
4. Matrix Factorization and Deep Learning/4. Matrix Factorization - Expanding Our Model.vtt	8.43 kB
4. Matrix Factorization and Deep Learning/5. Matrix Factorization - Regularization.mp4	22.35 MB
4. Matrix Factorization and Deep Learning/5. Matrix Factorization - Regularization.vtt	6.42 kB
4. Matrix Factorization and Deep Learning/6. Matrix Factorization - Exercise Prompt.mp4	4.25 MB
4. Matrix Factorization and Deep Learning/6. Matrix Factorization - Exercise Prompt.vtt	1.29 kB
4. Matrix Factorization and Deep Learning/7. Matrix Factorization in Code.mp4	52.37 MB
4. Matrix Factorization and Deep Learning/7. Matrix Factorization in Code.vtt	6.38 kB
4. Matrix Factorization and Deep Learning/8. Matrix Factorization in Code - Vectorized.mp4	97.37 MB
4. Matrix Factorization and Deep Learning/8. Matrix Factorization in Code - Vectorized.vtt	11.08 kB
4. Matrix Factorization and Deep Learning/9. SVD (Singular Value Decomposition).mp4	32.63 MB
4. Matrix Factorization and Deep Learning/9. SVD (Singular Value Decomposition).vtt	8.21 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/1. RBMs for Collaborative Filtering Section Introduction.mp4	10.32 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/1. RBMs for Collaborative Filtering Section Introduction.vtt	2.36 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/10. RBM Code pt 1.mp4	70.43 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/10. RBM Code pt 1.vtt	8.74 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/11. RBM Code pt 2.mp4	39.57 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/11. RBM Code pt 2.vtt	4.63 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/12. RBM Code pt 3.mp4	128.54 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/12. RBM Code pt 3.vtt	11.98 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/13. Speeding up the RBM Code.mp4	82.94 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/13. Speeding up the RBM Code.vtt	7.93 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/2. Intro to RBMs.mp4	39.44 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/2. Intro to RBMs.vtt	8.89 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/3. Motivation Behind RBMs.mp4	34.01 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/3. Motivation Behind RBMs.vtt	7.55 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/4. Intractability.mp4	12.92 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/4. Intractability.vtt	3.35 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/5. Neural Network Equations.mp4	31.70 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/5. Neural Network Equations.vtt	7.42 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/6. Training an RBM (part 1).mp4	49.06 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/6. Training an RBM (part 1).vtt	11.76 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/7. Training an RBM (part 2).mp4	27.33 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/7. Training an RBM (part 2).vtt	6.44 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/8. Training an RBM (part 3) - Free Energy.mp4	27.57 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/8. Training an RBM (part 3) - Free Energy.vtt	7.03 kB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/9. Categorical RBM for Recommender System Ratings.mp4	47.59 MB
5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering/9. Categorical RBM for Recommender System Ratings.vtt	12.05 kB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/1. Big Data and Spark Section Introduction.mp4	35.54 MB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/1. Big Data and Spark Section Introduction.vtt	8.24 kB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/2. Setting up Spark in your Local Environment.mp4	34.66 MB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/2. Setting up Spark in your Local Environment.vtt	8.58 kB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/3. Matrix Factorization in Spark.mp4	110.01 MB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/3. Matrix Factorization in Spark.vtt	9.71 kB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/4. Spark Submit.mp4	56.06 MB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/4. Spark Submit.vtt	6.95 kB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/5. Setting up a Spark Cluster on AWS EC2.mp4	73.23 MB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/5. Setting up a Spark Cluster on AWS EC2.vtt	13.75 kB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/6. Making Predictions in the Real World.mp4	11.31 MB
6. Big Data Matrix Factorization with Spark Cluster on AWS EC2/6. Making Predictions in the Real World.vtt	3.05 kB
7. Basics Review/1. Keras Discussion.mp4	27.64 MB
7. Basics Review/1. Keras Discussion.vtt	8.05 kB
7. Basics Review/2. Keras Neural Network in Code.mp4	66.17 MB
7. Basics Review/2. Keras Neural Network in Code.vtt	6.47 kB
7. Basics Review/3. Keras Functional API.mp4	38.64 MB
7. Basics Review/3. Keras Functional API.vtt	4.70 kB
7. Basics Review/4. Confidence Intervals (Appendix).mp4	39.83 MB
7. Basics Review/4. Confidence Intervals (Appendix).vtt	11.48 kB
7. Basics Review/5. Gaussian Conjugate Prior (Appendix).mp4	25.13 MB
7. Basics Review/5. Gaussian Conjugate Prior (Appendix).vtt	6.27 kB
8. Appendix/1. What is the Appendix.mp4	18.09 MB
8. Appendix/1. What is the Appendix.vtt	3.27 kB
8. Appendix/10. Python 2 vs Python 3.mp4	19.11 MB
8. Appendix/10. Python 2 vs Python 3.vtt	5.34 kB
8. Appendix/11. BONUS Where to get discount coupons and FREE deep learning material.mp4	13.37 MB
8. Appendix/11. BONUS Where to get discount coupons and FREE deep learning material.vtt	2.99 kB
8. Appendix/2. Windows-Focused Environment Setup 2018.mp4	194.28 MB
8. Appendix/2. Windows-Focused Environment Setup 2018.vtt	17.38 kB
8. Appendix/3. How to How to install Numpy, Theano, Tensorflow, etc....mp4	167.00 MB
8. Appendix/3. How to How to install Numpy, Theano, Tensorflow, etc....vtt	12.41 kB
8. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4	117.30 MB
8. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt	27.96 kB
8. Appendix/5. How to Succeed in this Course (Long Version).mp4	39.24 MB
8. Appendix/5. How to Succeed in this Course (Long Version).vtt	12.84 kB
8. Appendix/6. How to Code by Yourself (part 1).mp4	82.52 MB
8. Appendix/6. How to Code by Yourself (part 1).vtt	19.61 kB
8. Appendix/7. How to Code by Yourself (part 2).mp4	56.66 MB
8. Appendix/7. How to Code by Yourself (part 2).vtt	11.62 kB
8. Appendix/8. What order should I take your courses in (part 1).mp4	88.42 MB
8. Appendix/8. What order should I take your courses in (part 1).vtt	14.17 kB
8. Appendix/9. What order should I take your courses in (part 2).mp4	123.03 MB
8. Appendix/9. What order should I take your courses in (part 2).vtt	20.29 kB
Discuss.FreeTutorials.Us.html	165.68 kB
FreeCoursesOnline.Me.html	108.30 kB
FreeTutorials.Eu.html	102.23 kB
How you can help Team-FTU.txt	259.00 B
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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/