Recommender Systems And Deep Learning In Python

你会学到什么

• 使用简单和最先进的算法为您的用户理解和实施准确的建议
• 使用 AWS EC2 集群在 Spark 上进行大数据矩阵分解
• 纯 Numpy 中的矩阵分解/ SVD
• Keras中的矩阵分解
• Keras 中的深度神经网络、残差网络和自动编码器
• Tensorflow 中的受限玻尔兹曼机

课程介绍

信不信由你，今天几乎 所有 的在线企业都  以某种方式使用推荐系统。

我所说的“推荐系统”是什么意思，它们为什么有用？

让我们看看根据 Alexa 排名的互联网上排名前 3 的网站：Google、YouTube 和 Facebook。

推荐系统构成了这些技术的基础。

谷歌：搜索结果

这就是为什么谷歌是当今最成功的科技公司的原因。

YouTube：视频仪表板

我敢肯定，  当我有更重要的事情要做时，我不是唯一一个不小心在 YouTube 上花费数小时的人！他们是如何说服你这样做的？

这是正确的。推荐系统！

Facebook：如此强大以至于世界各国政府都担心新闻源对人们的影响太大！（又或者是担心失去自己的力量……嗯……）

惊人！

本课程包含大量技巧，可让推荐系统跨多个平台工作。

我们将研究流行的新闻提要算法，例如 Reddit、  Hacker News和 Google PageRank。

我们将研究  当今大量媒体公司正在使用的贝叶斯推荐技术。

但本课程不仅仅是关于新闻提要。

多年来，亚马逊、  Netflix和 Spotify等公司 一直在使用推荐功能向客户推荐产品、电影和音乐。

这些算法带来了 数十亿 美元的额外收入。

所以我向你保证，你将在这门课程中学到的东西是非常真实的、非常适用的，并且会对你的业务产生巨大的影响。

对于那些喜欢深入研究理论以了解事物如何 真正 运作的人，您知道这是我的专长，并且在本课程中不会缺少。我们将介绍最先进的算法，如 矩阵分解 和 深度学习 （利用 监督 和非 监督学习——自动编码器和受限玻尔兹曼机），您将学到一大堆改进基线结果的技巧。

作为奖励，我们还将了解如何  在 Spark中使用大数据执行矩阵分解。我们将使用 Amazon EC2 实例和 Amazon Web Services (AWS)创建一个集群。大多数其他课程和教程都着眼于 MovieLens 100k 数据集——这是微不足道的！我们的示例使用了 2000 万个 MovieLens。

无论您是在电子商务商店中销售产品，还是只是撰写博客，您都可以使用这些技术在正确的时间向您的用户展示正确的推荐。

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

Last updated 10/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.01 GB | Duration: 12h 31m

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

课程介绍

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.

“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)