In this exercise you will implement a Transformer model and several variants such as Encoder Transformers, Decoder Transformers, and Encoder-Decoder transformers. You will then use these as the basis ...

This code is designed to provide a deep understanding of the inner workings of Transformers Encoders, particularly focusing on the self-attention mechanism. Transformers are a crucial part of natural ...

In Part 1, we discussed how Transformers form the crux of NLP. So let’s take a look at autoregressive and sequence-to-sequence models. ... Sequence-to-sequence models combine the transformer’s encoder ...

But not all transformer applications require both the encoder and decoder module. For example, the GPT family of large language models uses stacks of decoder modules to generate text.

Each encoder has two layers – a self-attention layer and a feed-forward Neural Network. The decoder has both layers, but between them is an attention layer that helps it to focus on only the relevant ...

Occluded person re-identification (Re-ID) is a challenging task as persons are frequently occluded by various obstacles or other persons, especially in the crowd scenario. To address these issues, we ...