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rohan.ga | ||
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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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www.shaped.ai
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| | | | | Google Research's latest paper in December 2024 , "Titans: Learning to Memorize at Test Time" introduces a groundbreaking neural long-term memory module that learns to memorize historical context at test time, potentially revolutionizing how AI models handle extended sequential contexts. This innovative approach combines the strengths of recurrent models and attention mechanisms, enabling efficient processing of sequences beyond 2 million tokens while maintaining computational feasibility. | |
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www.nicktasios.nl
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| | | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In this first post, we will tr | ||
