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024 7 _a10.1007/979-8-8688-0540-0
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072 7 _aCOM004000
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100 1 _aGrigorov, Dilyan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986495
245 1 0 _aIntroduction to Python and Large Language Models
_h[electronic resource] :
_bA Guide to Language Models /
_cby Dilyan Grigorov.
250 _a1st ed. 2024.
264 1 _aBerkeley, CA :
_bApress :
_bImprint: Apress,
_c2024.
300 _aXXII, 380 p. 37 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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505 0 _aChapter 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 _aGain 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.
650 0 _aArtificial intelligence.
_958441
650 0 _aMachine learning.
_955059
650 0 _aPython (Computer program language).
_964608
650 1 4 _aArtificial Intelligence.
_958441
650 2 4 _aMachine Learning.
_955059
650 2 4 _aPython.
_947553
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9798868805394
776 0 8 _iPrinted edition:
_z9798868805417
856 4 0 _uhttps://doi.org/10.1007/979-8-8688-0540-0
912 _aZDB-2-CWD
912 _aZDB-2-SXPC
999 _c64432
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