This book takes the user through the journey of natural language processing starting from n-gram models to neural network architectures like RNN before it moves to the state-of-the-art technology today, which is known as the transformers. The book details out the transformer architecture and mainly explains the self-attention mechanism, which is the foundation of the transformer concept.

The book deals with the topic of transformers in depth with examples from different NLP areas like text generation, sentiment analysis, zero-shot learning, text summarization, etc. The book takes a deep dive into huggingface APIs and their usage to create simple Gradio-based applications. We will delve into details of not only using pretrained models but also how to fine-tune the existing models with our own datasets.

We cover models like BERT, GPT2, T5, etc., and showcase how these models can be used directly to create a different range of applications in the area of natural language processing and understanding.

The book doesn’t just limit the knowledge and exploration of transformers to NLP but also covers at a high level how transformers are being used in areas like vision.