/explore
|
You are here 
|
www.ntentional.com | ||
| | | | |
www.paepper.com
|
|
| | | | | Recent advances in training deep neural networks have led to a whole bunch of impressive machine learning models which are able to tackle a very diverse range of tasks. When you are developing such a model, one of the notable downsides is that it is considered a "black-box" approach in the sense that your model learns from data you feed it, but you don't really know what is going on inside the model. | |
| | | | |
polukhin.tech
|
|
| | | | | Pruning: Before and After | |
| | | | |
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... | |
| | | | |
kevinlynagh.com
|
|
| | | [AI summary] The author discusses their experience developing a simple neural network for sensor data processing on a microcontroller, highlighting challenges with quantization and inference optimization. | ||
