English | 2021 | ISBN: B09MMDT8LP | 503 pages | PDF EPUB | 53.6 MB
Learn how to build recommender systems from one of Amazon's pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation technologies. This updated second edition covers the latest developments in the field from Google and Amazon, and the latest research in applying deep neural networks to recommender systems.
You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them.
This book is adapted from Frank's popular online course published by Sundog Education, so you can expect lots of visual aids from its slides and a conversational, accessible tone throughout the book. The graphics and scripts from over 350 slides are included, and you'll have access to all of the source code associated with it as well.
We'll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from Frank's extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data.
This book is very hands-on; you'll develop your own framework for evaluating and combining many different recommendation algorithms together, and you'll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people.
We'll cover:
Building a recommendation engine
Evaluating recommender systems
Content-based filtering using item attributes
Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF
Model-based methods including matrix factorization and SVD
Applying deep learning, AI, and artificial neural networks to recommendations
Session-based recommendations with recursive neural networks
Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines
Using the Tensorflow Recommenders Framework (TFRS) to develop and deploy deep learning-based recommender systems
Using SaaS platforms such as Amazon Personalize, Recombee, and RichRelevance
Using Generative Adversarial Networks (GAN's) to generate user recommendations
Real-world challenges and solutions with recommender systems
Case studies from YouTube and Netflix
Building hybrid, ensemble recommenders
This comprehensive book takes you all the way from the early days of collaborative filtering, to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user
.
The coding exercises for this book use the Python programming language. We include an intro to Python if you're new to it, but you'll need some prior programming experience in order to use this book successfully. We also include a short introduction to deep learning, Tensorfow, and Keras if you are new to the field of artificial intelligence, but you'll need to be able to understand new computer algorithms.
Dive in, and learn about one of the most interesting and lucrative applications of machine learning and deep learning there is!
download скачать
https://nitroflare.com/view/59021CB12D6672F/rnn2n.Building.Recommender.Systems.with.Machine.Learning.and.AI.2nd.Edition.rar
https://rapidgator.net/file/c584b005b8d6f6a869746a3a12e8086c/rnn2n.Building.Recommender.Systems.with.Machine.Learning.and.AI.2nd.Edition.rar
