Training Deep Transformers
A novel approach is introduced for training deep transformers effectively on small datasets, challenging the conventional belief that large datasets are necessary. Key to this method is improved model initialization and the removal of layer normalization, allowing for stable training even with limited data. The insights shared highlight the importance of leveraging pre-trained models and adjusting training techniques to optimize performance.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Generating SQL [Database Queries] from Natural Language with Yanshuai Cao - #519
Related Questions