03804nam a22005175i 4500 979-8-8688-0540-0 DE-He213 20251030113242.0 cr nn 008mamaa 241023s2024 xxu| s |||| 0|eng d 9798868805400 979-8-8688-0540-0 10.1007/979-8-8688-0540-0 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Grigorov, Dilyan. author. aut http://id.loc.gov/vocabulary/relators/aut 86495 Introduction to Python and Large Language Models [electronic resource] : A Guide to Language Models / by Dilyan Grigorov. 1st ed. 2024. Berkeley, CA : Apress : Imprint: Apress, 2024. XXII, 380 p. 37 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Chapter 1: Evolution and Significance of Large Language Models -- Chapter 2: What Are Large Language Models? -- Chapter 3: Python for LLMs -- Chapter 4: Python and Other Programming Approaches -- Chapter 5: Basic overview of the components of the LLM architectures -- Chapter 6: Applications of LLMs in Python -- Chapter 7: Harnessing Python 3.11 and Python Libraries for LLM Development. Gain a solid foundation for Natural Language Processing (NLP) and Large Language Models (LLMs), emphasizing their significance in today’s computational world. This book is an introductory guide to NLP and LLMs with Python programming. The book starts with the basics of NLP and LLMs. It covers essential NLP concepts, such as text preprocessing, feature engineering, and sentiment analysis using Python. The book offers insights into Python programming, covering syntax, data types, conditionals, loops, functions, and object-oriented programming. Next, it delves deeper into LLMs, unraveling their complex components. You’ll learn about LLM elements, including embedding layers, feedforward layers, recurrent layers, and attention mechanisms. You’ll also explore important topics like tokens, token distributions, zero-shot learning, LLM hallucinations, and insights into popular LLM architectures such as GPT-4, BERT, T5, PALM, and others. Additionally, it covers Python libraries like Hugging Face, OpenAI API, and Cohere. The final chapter bridges theory with practical application, offering step-by-step examples of coded applications for tasks like text generation, summarization, language translation, question-answering systems, and chatbots. In the end, this book will equip you with the knowledge and tools to navigate the dynamic landscape of NLP and LLMs. You will: Understand the basics of Python and the features of Python 3.11 Explore the essentials of NLP and how do they lay the foundations for LLMs. Review LLM components. Develop basic apps using LLMs and Python. Artificial intelligence. 58441 Machine learning. 55059 Python (Computer program language). 64608 Artificial Intelligence. 58441 Machine Learning. 55059 Python. 47553 SpringerLink (Online service) Springer Nature eBook Printed edition: 9798868805394 Printed edition: 9798868805417 https://doi.org/10.1007/979-8-8688-0540-0 ZDB-2-CWD ZDB-2-SXPC 64432 64432