Explore >> Select a destination
|
You are here 
|
zongyi-li.github.io | ||
| | | | |
d2l.ai
|
|
| | | | | [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... | |
| | | | |
ataspinar.com
|
|
| | | | | [latexpage] In this blog-post we will have a look at how Differential Equations (DE) can be solved numerically via the Finite Differences method. By solving differential equations we can run simula... | |
| | | | |
michael-lewis.com
|
|
| | | | | This is a short summary of some of the terminology used in machine learning, with an emphasis on neural networks. I've put it together primarily to help my own understanding, phrasing it largely in non-mathematical terms. As such it may be of use to others who come from more of a programming than a mathematical background. | |
| | | | |
comsci.blog
|
|
| | | In this blog post, we will learn about vision transformers (ViT), and implement an MNIST classifier with it. We will go step-by-step and understand every part of the vision transformers clearly, and you will see the motivations of the authors of the original paper in some of the parts of the architecture. | ||
