Llms In Python

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Mastering Large Language Models With Python

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Author : Raj Arun R
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-04-12

Mastering Large Language Models With Python written by Raj Arun R and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-12 with Computers categories.

A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index

Unveiling Langchain And Llm For Python Developers

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Author : Matthew D Passmore
language : en
Publisher: Independently Published
Release Date : 2024-07-09

Unveiling Langchain And Llm For Python Developers written by Matthew D Passmore and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-09 with Computers categories.

Unlock the power of Language Models and revolutionize your web development skills with "Unveiling LangChain and LLM for Python Developers: Your Beginner-Friendly Guide to Building Intelligent, Scalable, and Unique Web Applications (LLMs Decoded with TensorFlow, Hugging Face, and More)." In this comprehensive guide, dive into the world of Large Language Models (LLMs) and learn how to leverage their capabilities to create cutting-edge web applications. Whether you're a seasoned developer or just starting your journey, this book offers a clear and practical approach to mastering LLMs using popular frameworks like TensorFlow and Hugging Face. **What You'll Discover: ** - **Foundations of LLMs**: Understand the basics of language models, their architectures, and how they process and generate human-like text. - **Hands-On Tutorials**: Step-by-step instructions to integrate LLMs into your Python projects, complete with code examples and detailed explanations. - **Scalable Solutions**: Learn how to build applications that can handle large-scale data and deliver real-time performance. - **Advanced Techniques**: Explore sophisticated topics such as fine-tuning pre-trained models, optimizing performance, and deploying LLMs in production environments. - **Practical Applications**: Real-world case studies demonstrating how LLMs can be used in chatbots, content generation, sentiment analysis, and more. With a focus on practical knowledge and real-world applications, this book equips you with the skills to create intelligent, scalable, and unique web applications that stand out in today's competitive landscape. Whether you're aiming to enhance user experience, automate content creation, or simply explore the potential of artificial intelligence in web development, "Unveiling LangChain and LLM for Python Developers" is your essential guide to the future of web development

Llms With Python

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Author : Sam Coded
language : en
Publisher: Independently Published
Release Date : 2025-11-14

Llms With Python written by Sam Coded and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-14 with Computers categories.

The landscape of artificial intelligence has undergone a profound transformation in the past decade, one that has elevated large language models from experimental curiosities to indispensable components of modern technology. What began as modest neural networks capable of predicting the next word in a sentence has evolved into sophisticated systems that generate coherent essays, translate languages with near-human fluency, write functional code, and engage in nuanced conversations. At the center of this revolution stands Python, a programming language whose simplicity, versatility, and rich ecosystem have made it the de facto standard for artificial intelligence development. This book, LLMs with Python: The New Edition, serves as a comprehensive guide to mastering these powerful models through the lens of Python, bridging theoretical understanding with practical implementation. The emergence of large language models represents more than a technical achievement; it signals a paradigm shift in how humans interact with machines. Applications that once required extensive rule-based programming or specialized expertise can now be constructed with a few lines of Python code interfacing with a pre-trained model. A customer service chatbot that understands context across multiple turns of dialogue, a legal assistant that summarizes contracts while highlighting potential risks, or a creative tool that drafts marketing copy tailored to specific demographics-these are no longer futuristic visions but everyday realities built by developers using Python libraries such as Transformers, LangChain, and PyTorch. The accessibility of these tools has democratized artificial intelligence, enabling individuals and small teams to create solutions that rival those of large corporations. Understanding why Python has become the universal language for artificial intelligence requires examining its historical trajectory and technical advantages. Introduced in 1991 by Guido van Rossum, Python was designed with readability and ease of use as core principles. Its syntax, which emphasizes indentation over braces and favors expressive one-liners, allows developers to focus on solving problems rather than wrestling with language complexity. In the context of machine learning and artificial intelligence, this clarity becomes particularly valuable when working with complex mathematical concepts such as gradient descent, attention mechanisms, or loss functions. A single line of Python using the Hugging Face Transformers library can load a model with billions of parameters, whereas equivalent functionality in lower-level languages like C++ might require thousands of lines of code. The ecosystem surrounding Python has grown in parallel with the rise of large language models. The Python Package Index (PyPI) hosts over 400,000 packages, many specifically tailored for artificial intelligence development. Libraries such as NumPy and Pandas provide efficient data manipulation, Matplotlib and Seaborn enable visualization of training metrics, and scikit-learn offers classical machine learning algorithms that complement deep learning approaches. More importantly, the deep learning frameworks-TensorFlow, PyTorch, and JAX-all provide Python interfaces as their primary means of interaction. This convergence means that a developer learning to fine-tune a language model is simultaneously building proficiency in the broader Python data science stack, creating transferable skills across the artificial intelligence domain.

Introduction To Python And Large Language Models

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Author : Dilyan Grigorov
language : en
Publisher: Springer Nature
Release Date : 2024-10-22

Introduction To Python And Large Language Models written by Dilyan Grigorov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-22 with Computers categories.

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. What You’ll Learn 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. Who This Book Is For Data analysts, AI and Machine Learning Experts, Python developers, and Software Development Professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks.

Llms In Python

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Author : Finn Cordex
language : en
Publisher: Independently Published
Release Date : 2025-11-11

Llms In Python written by Finn Cordex and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-11 with Computers categories.

Unlock the full engineering power of Large Language Models with Python. In LLMs in Python (2026 Edition), acclaimed AI engineer and author Finn Cordex delivers a hands-on, expert-level guide to designing, building, fine-tuning, and deploying modern language models. This is not another beginner's tutorial-it's a complete engineering playbook packed with 50+ real-world Python projects that reveal exactly how today's most advanced AI systems are built. From core transformer theory to multi-agent workflows and Retrieval-Augmented Generation (RAG), every chapter blends deep technical insight with practical, runnable code. You'll move step-by-step through building and scaling production-ready LLM systems using LangChain, LangGraph, Python, and state-of-the-art open-source frameworks. What You'll Learn Master the architecture and inner workings of Large Language Models Build and train LLMs from scratch using modern Python toolchains Fine-tune and optimize models with LoRA, PEFT, and transfer-learning methods Create advanced LangChain pipelines for multi-step reasoning and agentic AI Implement LangGraph for context-aware, structured decision workflows Design Retrieval-Augmented Generation (RAG) systems that ground LLMs in data Deploy, scale, and monitor production-grade LLMs in cloud environments Explore 50+ hands-on projects that reinforce every concept through real-world use cases Who This Book Is For This book is written for developers, data scientists, and AI engineers who already know Python and want to move beyond theory into true LLM engineering mastery. Whether you're building enterprise AI systems, autonomous agents, or custom language applications, you'll find actionable techniques, expert commentary, and deployable code ready to use in your own projects. Why This Book Stands Out Expert-Level Projects: Each project builds on the last, guiding you from fundamental model construction to multi-agent AI design. Cutting-Edge Frameworks: Covers LangChain, LangGraph, RAG, and modern agentic patterns. Up-to-Date for 2026: Reflects the latest breakthroughs in LLM architecture, fine-tuning, and open-source tooling. Engineer's Perspective: Written by Finn Cordex-an author known for bridging research theory with hands-on, production-grade AI engineering. If you're serious about mastering Large Language Models in Python, this is your definitive guide. Build, deploy, and scale next-generation AI systems with confidence.

From Ml Algorithms To Genai Llms

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Author : Aman Kharwal
language : en
Publisher:
Release Date : 2024-10-22

From Ml Algorithms To Genai Llms written by Aman Kharwal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-22 with Computers categories.

From ML Algorithms to GenAI & LLMs, Written by Aman Kharwal, founder of Statso.io, is the second edition of the book - Machine Learning Algorithms: Handbook. This book offers a comprehensive and expanded guide through the evolving world of machine learning and generative AI. Whether you are an experienced data scientist or just starting, this edition delivers practical insights and clear explanations of essential concepts like regression, classification, clustering, deep learning, and time series forecasting. This edition introduces two new chapters: "Mastering GenAI and LLMs" and "Understanding GANs for Generative AI with a Hands-on Project", which provide deep dives into large language models and generative adversarial networks (GANs). With hands-on Python code snippets and real-world project examples, the book bridges the gap between theory and application, offering you the tools to apply machine learning techniques effectively. Additional highlights include performance evaluation methods, data preprocessing techniques, feature engineering, and a quick reference appendix for tuning machine learning models. The book equips you with the necessary skills to navigate modern machine learning and AI, which makes it an essential resource for anyone interested in the field.

Generative Ai With Langchain

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Author : Ben Auffarth
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-12-22

Generative Ai With Langchain written by Ben Auffarth and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-22 with Computers categories.

2024 Edition – Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository. Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Learn how to leverage LangChain to work around LLMs’ inherent weaknesses Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learn Create LLM apps with LangChain, like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is for The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.

Building Llm Applications With Python

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Author : ANAND. VEMULA
language : en
Publisher:
Release Date : 2024

Building Llm Applications With Python written by ANAND. VEMULA and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.

Building Llm Powered Applications

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Author : Valentina Alto
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-22

Building Llm Powered Applications written by Valentina Alto and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-22 with Computers categories.

Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Embed LLMs into real-world applications Use LangChain to orchestrate LLMs and their components within applications Grasp basic and advanced techniques of prompt engineering Book DescriptionBuilding LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.What you will learn Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM Use AI orchestrators like LangChain, with Streamlit for the frontend Get familiar with LLM components such as memory, prompts, and tools Learn how to use non-parametric knowledge and vector databases Understand the implications of LFMs for AI research and industry applications Customize your LLMs with fine tuning Learn about the ethical implications of LLM-powered applications Who this book is for Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.

Learn Generative Ai With Pytorch

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Author : Mark Liu
language : en
Publisher: Simon and Schuster
Release Date : 2025-01-28

Learn Generative Ai With Pytorch written by Mark Liu and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-28 with Computers categories.

Learn how generative AI works by building your very own models that can write coherent text, create realistic images, and even make lifelike music. Learn Generative AI with PyTorch teaches the underlying mechanics of generative AI by building working AI models from scratch. Throughout, you’ll use the intuitive PyTorch framework that’s instantly familiar to anyone who’s worked with Python data tools. Along the way, you’ll master the fundamentals of General Adversarial Networks (GANs), Transformers, Large Language Models (LLMs), variational autoencoders, diffusion models, LangChain, and more! In Learn Generative AI with PyTorch you’ll build these amazing models: • A simple English-to-French translator • A text-generating model as powerful as GPT-2 • A diffusion model that produces realistic flower images • Music generators using GANs and Transformers • An image style transfer model • A zero-shot know-it-all agent The generative AI projects you create use the same underlying techniques and technologies as full-scale models like GPT-4 and Stable Diffusion. You don’t need to be a machine learning expert—you can get started with just some basic Python programming skills. About the technology Transformers, Generative Adversarial Networks (GANs), diffusion models, LLMs, and other powerful deep learning patterns have radically changed the way we manipulate text, images, and sound. Generative AI may seem like magic at first, but with a little Python, the PyTorch framework, and some practice, you can build interesting and useful models that will train and run on your laptop. This book shows you how. About the book Learn Generative AI with PyTorch introduces the underlying mechanics of generative AI by helping you build your own working AI models. You’ll begin by creating simple images using a GAN, and then progress to writing a language translation transformer line-by-line. As you work through the fun and fascinating projects, you’ll train models to create anime images, write like Hemingway, make music like Mozart, and more. You just need Python and a few machine learning basics to get started. You’ll learn the rest as you go! What's inside • Build an English-to-French translator • Create a text-generation LLM • Train a diffusion model to produce high-resolution images • Music generators using GANs and Transformers About the reader Examples use simple Python. No deep learning experience required. About the author Mark Liu is the founding director of the Master of Science in Finance program at the University of Kentucky. The technical editor on this book was Emmanuel Maggiori.