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embecosm.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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josephm.dev
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| | | | | Fine tune a machine learning model to identify cloud types. | |
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coornail.net
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| | | | | Neural networks are a powerful tool in machine learning that can be trained to perform a wide range of tasks, from image classification to natural language processing. In this blog post, well explore how to teach a neural network to add together two numbers. You can also think about this article as a tutorial for tensorflow. | |
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distill.pub
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| | | What we'd like to find out about GANs that we don't know yet. | ||
