The latent factor and matrix factorization models are limited to capture non-linear patterns of the user and latent spaces. However, VAE[1] solve this problem by finding the latent factor distribution after projecting User or Item Ratings, and using the latent factor distribution to generate the corresponding rating predictions.
Summary for application and theory for NLP baseline model. Sample code please referred to My github.
Even we do not use them today, however, learning the baseline models are important for understanding on how to encode and decode a language, which is the priority of NLP.
Divide-and-conquer is easy to understand, but hard to create. This article will discuss the key for proofing Divide-and-conquer as well as some ideas for solving a new problem using divide-and-conquer based on these 4 topics: Merge Sort, Quick Sort, Hanoi and Closet Point
Basic Deep Learning math for Coursera Course Deep Learning by Andrew Ng, Kian Katanforoosh and Younes Bensouda Mourri.
Please expect some loading time due to math formula.