AI has acquired startling new language capabilities in just the past few years. Driven by the
rapid advances in deep learning language AI systems are able to write and understand text
better than ever before. This trend enables the rise of new features products and entire
industries. Through the visually educational nature of this book Python developers will learn
the practical tools and concepts they need to use these capabilities today. You'll learn how to
use the power of pre-trained large language models for use cases like copywriting and
summarization create semantic search systems that go beyond keyword matching build systems
that classify and cluster text to enable scalable understanding of large numbers of text
documents and use existing libraries and pre-trained models for text classification search
and clusterings. This book also shows you how to: Build advanced LLM pipelines to cluster text
documents and explore the topics they belong to Build semantic search engines that go beyond
keyword search with methods like dense retrieval and rerankers Learn various use cases where
these models can provide value Understand the architecture of underlying Transformer models
like BERT and GPT Get a deeper understanding of how LLMs are trained Optimize LLMs for specific
applications with methods such as generative model fine-tuning contrastive fine-tuning and
in-context learning Jay Alammar is Director and Engineering Fellow at Cohere (pioneering
provider of large language models as an API). Maarten Grootendorst is a Senior Clinical Data
Scientist at Netherlands Comprehensive Cancer Organization (IKNL).
