Llms In Python
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Mastering Large Language Models with Python
Author: Raj Arun R
language: en
Publisher: Orange Education Pvt Ltd
Release Date: 2024-04-12
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
Author: Matthew D Passmore
language: en
Publisher: Independently Published
Release Date: 2024-07-09
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
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.