Hands-On Large Language Models : Language Understanding And Generation

  AI has achieved remarkable advancements in language understanding and generation in recent years, thanks to rapid progress in deep learning. These language AI systems now possess the ability to write and comprehend text with unprecedented proficiency, paving the way for innovative features, products, and industries. This book provides Python developers with the practical tools and concepts necessary to harness these capabilities effectively. You’ll discover how to leverage pre-trained large language models for tasks such as copywriting and summarization, create semantic search systems that transcend keyword matching, and construct systems that classify and cluster text for scalable understanding of vast text datasets. You’ll also learn to utilize existing libraries and pre-trained models for text classification, search, and clustering. This book also guides you through: Building advanced LLM pipelines to cluster text documents and explore their topics
Creating semantic search engines that surpass keyword search using techniques like dense retrieval and rerankers
Exploring various valuable use cases for these models
Understanding the architecture of underlying Transformer models such as BERT and GPT
Gaining insights into LLM training methodologies
Optimizing LLMs for specific applications through generative model fine-tuning, contrastive fine-tuning, and in-context learning
Jay Alammar is Director and Engineering Fellow at Cohere (a leading provider of large language models as an API). Maarten Grootendorst is a Senior Clinical Data Scientist at Netherlands Comprehensive Cancer Organization (IKNL).

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