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 www.alfredo.motta.name
Compute property recommendations: A collaborative filtering approach - Alfredo Motta
| | ml-ops.org
Three Levels of ML Software
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| | Machine Learning Operations
| | d2l.ai
21. Recommender Systems - Dive into Deep Learning 1.0.3 documentation
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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...
| | dzone.com
Need Billions of Web Pages? Don't bother Crawling.
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| | CommonCrawl is an organization which provides web crawl data for free. Read on to find out about CommonCrawl and how it can help your team.
| | jhui.github.io
"Deep learning - Computation & optimization."
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| [AI summary] The provided text discusses various mathematical and computational concepts relevant to deep learning, including poor conditioning in matrices, underflow/overflow in softmax functions, Jacobian and Hessian matrices, learning rate optimization using Taylor series, Newton's method, saddle points, constrained optimization with Lagrange multipliers, and KKT conditions. These concepts are crucial for understanding numerical stability, optimization algorithms, and solving constrained problems in machine learning.