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ai.googleblog.com | ||
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d2l.ai
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| | | | | [AI summary] This chapter provides an in-depth exploration of recommender systems, covering fundamental concepts and advanced techniques. It begins with an overview of collaborative filtering and the distinction between explicit and implicit feedback. The chapter then delves into various recommendation tasks and their evaluation methods. It introduces the MovieLens dataset as a practical example for building recommendation models. Subsequent sections discuss matrix factorization, AutoRec using autoencoders, personalized ranking with Bayesian personalized ranking and hinge loss, neural collaborative filtering, sequence-aware recommenders, feature-rich models, and deep factorization machines like DeepFM. The chapter concludes with implementation details and ev... | |
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research.google
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| | | | | Posted by Bryan Perozzi, Research Scientist and Qi Zhu, Research Intern, Google Research Graph Neural Networks (GNNs) are powerful tools for levera... | |
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www.altexsoft.com
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| | | | | A dive into the machine learning pipeline on the production stage: the description of architecture, tools, and general flow of the model deployment. | |
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teddykoker.com
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| | | In this post we will be using a method known as transfer learning in order to detect metastatic cancer in patches of images from digital pathology scans. | ||
