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所在平台: Udemy |
课程主页: https://www.udemy.com/course/recommender-systems/
课程评论：没有评论
课程名称：Python中的推荐系统与深度学习 课程概述：现代在线商业几乎都以某种方式运用推荐系统。推荐系统的本质及其带来的价值将通过对Google、YouTube和Facebook这三大网站的分析阐明。推荐系统是这些技术的基础，Google借助搜索结果的推荐系统而获得成功；YouTube则通过推荐视频让用户沉迷；Facebook的新闻推送功能强大到引起了各国政府的关注。 本课程将传授让推荐系统在多个平台上运作的策略，包括流行的新闻推送算法（如Reddit、Hacker News和Google PageRank），以及众多媒体公司采用的贝叶斯推荐技术。同时，我们也会探讨亚马逊、Netflix和Spotify等公司如何利用推荐系统来增加产品、电影和音乐的销售。 课程将涵盖现代算法，如矩阵分解和深度学习（包括监督学习和无监督学习技术，如自编码器和限制玻尔兹曼机），并教你如何改进基线结果。此外，我们还将使用Spark进行大数据的矩阵分解，创建AWS上的Amazon EC2集群，以处理MovieLens 2000万的数据集，远超于常见的100k数据集。 无论是电子商务店铺的产品推荐，还是个人博客的文章推荐，学员都能够运用这些技术实时给用户提供精准推荐。本课程强调理论与实践结合，确保学员真正掌握实现机器学习算法的方法。 建议学员提前具备一些基础的数学知识，以便更深入地理解高级部分内容，并熟练使用Python和Numpy，深度学习部分需要了解Keras基本使用，RBM部分需掌握TensorFlow。 独特优势包括每行代码的详细解释，杜绝浪费时间在不必要的打字上，同时课程不回避大学水平的数学，确保为学员讲解那些其他课程可能遗漏的重要算法细节。
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 resultsThey are why Google is the most successful technology company today.YouTube: Video dashboardI'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 scratchOther 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 arithmeticFor advanced sections, know calculus, linear algebra, and probability for a deeper understandingBe proficient in Python and the Numpy stack (see my free course)For the deep learning section, know the basics of using KerasFor the RBM section, know TensorflowWHAT 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)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out
