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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. With 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 pretrained 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 pretrained models for text classification, search, and clusterings. Part 1: Preview of Language AI Use Cases (unavailable) Preview of Language AI Use Cases (unavailable) Categorizing Text Neural Search Text Clustering and Topic Modeling Text Generation with GPT Models Recognizing and Extracting Entities (unavailable) Multimodal Large Language Models Part 2: Creating Language AI Models and Systems (unavailable) Tokens and Token Embeddings Looking Inside Large Language Models (unavailable) Fine-Tuning (unavailable) Building a GPT Model from Scratch (unavailable) Fine-Tuning Generation Models (unavailable) Creating Text Embedding Models Appendix A: Essential Python Data Tools (unavailable)
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https://frdl.to/cemvauk84ivw/Alammar_J._Hands-On_Large_Language_Models_6ed_2024_Eariy_Release.zip.html
