Our largest model has 6. 4 billion parameters and trains in less than 35 hours on a single p3. 16x machine.
In this paper, we study RF techniques based on both long-term and short-term context dependencies in multi-page product search.
To address these issues, we train a deep learning model for semantic matching using customer behavior data.
The problem is becoming more severe as deep learning models continue to grow larger in order to learn from complex, large-scale datasets.
Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired.
We present a personalized recommender system using neural network for recommending products, such as eBooks, audio-books, Mobile Apps, Video and Music.