This section explores the key components of the Transformer architecture, including input embedding, positional encoding, encoder and decoder layers, and the model training and inference processes.

We dive into Transformers in Deep Learning, a revolutionary architecture that powers today's cutting-edge models like GPT and BERT. We’ll break down the core concepts behind attention mechanisms, self ...

Deep tech Understanding Transformers, the machine learning model behind GPT-3 How this novel neural network architecture changes the way we analyze complex data types May 22, 2021 - 10:00 am ...

In this paper, we propose a transformer-based MRC-TransUNet framework, which effectively combines the advantages of transformer as well as CNN, reduces the number of parameters of traditional ...