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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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bdtechtalks.com
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| | | | | The transformer model has become one of the main highlights of advances in deep learning and deep neural networks. | |
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swethatanamala.github.io
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| | | | | The authors developed a straightforward application of the Long Short-Term Memory (LSTM) architecture which can solve English to French translation. | |
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simonwillison.net
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| | | Retrieval Augmented Generation (RAG) is a technique for adding extra "knowledge" to systems built on LLMs, allowing them to answer questions against custom information not included in their training data. ... | ||
