Preface

Transformer-based language models have emerged as a cornerstone in the field of Natural Language Processing (NLP), representing a paradigm shift. Their superior fine-tuning and zero-shot abilities have proven to be faster and more precise, surpassing the performance of traditional machine learning methods in various complex natural language tasks. This practical guide to NLP is a valuable resource for developers, familiarizing them with the Transformers architecture.

This book offers clear, step-by-step explanations of crucial concepts, supplemented with practical examples. We start with an easy-to-understand overview of the revolution in NLP. This includes a basic understanding of relevant deep learning concepts and technologies, along with comprehensive guidance on managing various NLP tasks.

This book is also highly beneficial for developers looking to broaden their understanding of multimodal models and generative AI. Transformers are not only used for NLP tasks but are also increasingly employed in computer vision tasks, signal processing, and many other areas. Besides NLP, there’s a fast-growing area of multimodal learning and generative AI that’s been showing some exciting progress. We’re talking about things such as GPT-4, Gemini, Claude, DALL-E, and Stable Diffusion-based models here. If you’re a developer, it’s worth keeping an eye on these technologies to see how you can best utilize them for your specific needs.