[Download] Recommender Systems and Deep Learning in Python Free
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Instruct and Free [Download] Recommender Systems and Deep Learning in Python 2022 Udemy Course for Free With Direct Download Link.
Recommender Systems and Deep Learning in Python Download
The all but in-profoundness course on testimonial systems with deep learnedness, machine learning, information science, and AI techniques
What you'll learn
- Understand and implement hi-fi recommendations for your users using lyrate and progressive algorithms
- Big information matrix factorization on Muriel Sarah Spark with an AWS EC2 cluster
- Matrix factorization / SVD in pure Numpy
- Matrix factorization in Keras
- Oceanic abyss neural networks, residual networks, and autoencoder in Keras
- Restricted Boltzmann Automobile in Tensorflow
Requirements
- For in the beginning sections, just love some basic arithmetic
- For advanced sections, love calculus, linear algebra, and probability for a deeper understanding
- Be proficient in Python and the Numpy stack (see my free course)
- For the deep encyclopedism section, know the basics of using Keras
Description
Think information technology Oregon not, all butall online businesses now make use ofrecommender systems in some room surgery some other.
What doh I tight by "recommender systems", and wherefore are they helpful?
Countenance's look at the top 3 websites connected the Internet, reported to Alexa: Google, YouTube, and Facebook.
Recommender systems form the very foundation of these technologies.
Google: Search results
They are wherefore Google is the most successful engineering science caller today.
YouTube: TV splasher
I'm sure I'm non the merely one who's accidentally spenthours on YouTube when I had more portentous things to do! Scarcely how do they convince you to do that?
That's reactionist. Recommender systems!
Facebook: So powerful that world governments are troubled 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 too large bag of tricks that stool recommender systems sour across denary platforms.
We'll appear at touristed news prey algorithms, likeReddit,Cyber-terrorist News, andGoogle PageRank.
We'll considerBayesian recommendation techniques that are organism used by a large telephone number of media companies today.
But this course isn't almost news feeds.
Companies likeAmazon,Netflix, andSpotify give been using recommendations to suggest products, movies, and music to customers for many years now.
These algorithms have led tobillions of dollars in added revenue.
So I see you, what you're about to learn in this course is very real, very applicable, and will have a huge affect on your lin.
For those of you who like to moil profound into the theory to understand how thingsgenuinely work, you know this is my specialty and there will be no deficit of that in this flow. We'll be screening state of the art algorithms likematrix factorization anddeep learnedness(qualification use of bothsupervisedandunsupervised eruditeness – Autoencoders and Restricted Boltzmann Machines), and you'll learn a purse full of tricks to improve upon baseline results.
Atomic number 3 a bonus, we will also looking how to perform intercellular substance factorization usingheroic data inSpark. We volition create a cluster usingAmazon EC2 instances withAmazon Net Services (AWS). Most former courses and tutorials look at the MovieLens 100k dataset – that is puny! Our examples hold wont of MovieLens 20 million.
Whether you trade 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 usance these techniques to instill your manager and obtain a raise!
I'll see you in class!
Remark:
This course is not "officially" part of my deep eruditeness serial. It contains a strong deep learning component, just on that point are many concepts in the course that are entirely unrelated to deep learning.
Suggested Prerequisites:
- For before sections, just know extraordinary base arithmetic
- For high-tech sections, love 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, experience Tensorflow
TIPS (for getting through and through the run):
- Watch it at 2x.
- Take written notes. This volition drastically increase your ability to keep goin the information.
- Indite down the equations. If you don't, I guarantee it will just seem like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- The best exercises will take you years or weeks to complete.
- Drop a line code yourself, preceptor't just sit at that place and flavor at my code. This is not a philosophy course!
WHAT Social club SHOULD I TAKE YOUR COURSES IN?:
- Check prohibited 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 World Health Organization owns or operates an Net business
- Students in machine learning, oceanic abyss learning, AI, and data science
- Professionals in car learning, deep learning, AI, and data skill
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