Kane - Building Recommender Systems with Machine Learning and AI: Help people discover new products and content with deep learning, neural networks, and machine learning recommendations

Learn how to build recommender systems from one of Amazons pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazons personalized product recommendation technologies.Youve seen automated recommendations everywhere - on Netflixs 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, youll become very valuable to them.This book is adapted from Franks 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 300 slides are included, and youll have access to all of the source code associated with it as well.Well 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, youll learn from Franks extensive industry experience to understand the real-world challenges youll encounter when applying these algorithms at large scale and with real-world data.This book is very hands-on; youll develop your own framework for evaluating and combining many different recommendation algorithms together, and youll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people. Well 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-Real-world challenges and solutions with recommender systems-Case studies from YouTube and Netflix-Building hybrid, ensemble recommendersThis 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 youre new to it, but youll 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 youll 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!