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Introduction to Python and large lan...

Grigorov, Dilyan.

Introduction to Python and large language modelsa guide to language models /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to Python and large language modelsby Dilyan Grigorov.
Reminder of title:	a guide to language models /
Author:	Grigorov, Dilyan.
Published:	Berkeley, CA :Apress :2024.
Description:	xxii, 380 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Natural language processing (Computer science)
Online resource:	https://doi.org/10.1007/979-8-8688-0540-0
ISBN:	9798868805400$q(electronic bk.)

Introduction to Python and large language modelsa guide to language models /
Grigorov, Dilyan.

Introduction to Python and large language modelsa guide to language models /[electronic resource] :by Dilyan Grigorov. - Berkeley, CA :Apress :2024. - xxii, 380 p. :ill., digital ;24 cm.

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.

ISBN: 9798868805400$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-0540-0doiSubjects--Topical Terms:

200539
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Introduction to Python and large language modelsa guide to language models /
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505 0 $a 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.
520 $a 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.
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