Building LLM Applications with Python: A Practical Guide
Building LLM Applications with Python: A Practical Guide

Author: Anand Vemula

Publisher: Anand Vemula

Published:

Total Pages: 42

ISBN-13:

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This book equips you to harness the remarkable capabilities of Large Language Models (LLMs) using Python. Part I unveils the world of LLMs. You'll delve into their inner workings, explore different LLM types, and discover their exciting applications in various fields. Part II dives into the practical side of things. We'll guide you through setting up your Python environment and interacting with LLMs. Learn to craft effective prompts to get the most out of LLMs and understand the different response formats they can generate. Part III gets you building! We'll explore how to leverage LLMs for creative text generation, from poems and scripts to code snippets. Craft effective question-answering systems and build engaging chatbots – the possibilities are endless! Part IV empowers you to maintain and improve your LLM creations. We'll delve into debugging techniques to identify and resolve issues. Learn to track performance and implement optimizations to ensure your LLM applications run smoothly. This book doesn't shy away from the bigger picture. The final chapter explores the ethical considerations of LLMs, addressing bias and promoting responsible use of this powerful technology. By the end of this journey, you'll be equipped to unlock the potential of LLMs with Python and contribute to a future brimming with exciting possibilities.

Building Data-Driven Applications with LlamaIndex
Building Data-Driven Applications with LlamaIndex

Author: Andrei Gheorghiu

Publisher: Packt Publishing Ltd

Published: 2024-05-10

Total Pages: 368

ISBN-13: 1805124404

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Solve real-world problems easily with artificial intelligence (AI) using the LlamaIndex data framework to enhance your LLM-based Python applications Key Features Examine text chunking effects on RAG workflows and understand security in RAG app development Discover chatbots and agents and learn how to build complex conversation engines Build as you learn by applying the knowledge you gain to a hands-on project Book DescriptionDiscover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations’. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.What you will learn Understand the LlamaIndex ecosystem and common use cases Master techniques to ingest and parse data from various sources into LlamaIndex Discover how to create optimized indexes tailored to your use cases Understand how to query LlamaIndex effectively and interpret responses Build an end-to-end interactive web application with LlamaIndex, Python, and Streamlit Customize a LlamaIndex configuration based on your project needs Predict costs and deal with potential privacy issues Deploy LlamaIndex applications that others can use Who this book is for This book is for Python developers with basic knowledge of natural language processing (NLP) and LLMs looking to build interactive LLM applications. Experienced developers and conversational AI developers will also benefit from the advanced techniques covered in the book to fully unleash the capabilities of the framework.

Python for Finance Cookbook
Python for Finance Cookbook

Author: Eryk Lewinson

Publisher: Packt Publishing Ltd

Published: 2020-01-31

Total Pages: 426

ISBN-13: 1789617324

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Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.

Generative AI with LangChain
Generative AI with LangChain

Author: Ben Auffarth

Publisher: Packt Publishing Ltd

Published: 2023-12-22

Total Pages: 369

ISBN-13: 1835088368

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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. Purchase of the print or Kindle book includes a free PDF eBook. 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.

Practical Natural Language Processing
Practical Natural Language Processing

Author: Sowmya Vajjala

Publisher: O'Reilly Media

Published: 2020-06-17

Total Pages: 455

ISBN-13: 149205402X

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Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective

The Well-Grounded Python Developer
The Well-Grounded Python Developer

Author: Doug Farrell

Publisher: Simon and Schuster

Published: 2023-07-18

Total Pages: 294

ISBN-13: 1617297445

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The Well-Grounded Python Developer teaches you how to write real software in Python by building on the basic language skills you already have. When you’re new to Python, it can be tough to understand where and how to use its many language features. There’s a dizzying array of libraries, and it’s challenging to fit everything together. The Well-Grounded Python Developer builds on Python skills you’ve learned in isolation and shows you how to unify them into a meaningful whole. The Well-Grounded Python Developer teaches you how to write real software in Python by building on the basic language skills you already have. It helps you see the big picture you can create out of small pieces, introducing concepts like modular construction, APIs, and the design of a basic web server. When you’re finished, you’ll have gone from having a basic understanding of Python's syntax, grammar, and libraries to using them as the tools of a professional software developer. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Optimizing Large Language Models Practical Approaches and Applications of Quantization Technique
Optimizing Large Language Models Practical Approaches and Applications of Quantization Technique

Author: Anand Vemula

Publisher: Anand Vemula

Published: 2024-08-19

Total Pages: 143

ISBN-13:

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The book provides an in-depth understanding of quantization techniques and their impact on model efficiency, performance, and deployment. The book starts with a foundational overview of quantization, explaining its significance in reducing the computational and memory requirements of LLMs. It delves into various quantization methods, including uniform and non-uniform quantization, per-layer and per-channel quantization, and hybrid approaches. Each technique is examined for its applicability and trade-offs, helping readers select the best method for their specific needs. The guide further explores advanced topics such as quantization for edge devices and multi-lingual models. It contrasts dynamic and static quantization strategies and discusses emerging trends in the field. Practical examples, use cases, and case studies are provided to illustrate how these techniques are applied in real-world scenarios, including the quantization of popular models like GPT and BERT.

Fluent Python
Fluent Python

Author: Luciano Ramalho

Publisher: "O'Reilly Media, Inc."

Published: 2015-07-30

Total Pages: 755

ISBN-13: 1491946253

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Python’s simplicity lets you become productive quickly, but this often means you aren’t using everything it has to offer. With this hands-on guide, you’ll learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—features. Author Luciano Ramalho takes you through Python’s core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time. Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3. This book covers: Python data model: understand how special methods are the key to the consistent behavior of objects Data structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode age Functions as objects: view Python functions as first-class objects, and understand how this affects popular design patterns Object-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritance Control flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packages Metaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work

Deep Learning for Coders with fastai and PyTorch
Deep Learning for Coders with fastai and PyTorch

Author: Jeremy Howard

Publisher: O'Reilly Media

Published: 2020-06-29

Total Pages: 624

ISBN-13: 1492045497

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Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Building AI Intensive Python Applications
Building AI Intensive Python Applications

Author: Rachelle Palmer

Publisher: Packt Publishing Ltd

Published: 2024-09-06

Total Pages: 299

ISBN-13: 1836207247

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Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI apps Key Features Get to grips with the fundamentals of LLMs, vector databases, and Python frameworks Implement effective retrieval-augmented generation strategies with MongoDB Atlas Optimize AI models for performance and accuracy with model compression and deployment optimization Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.What you will learn Understand the architecture and components of the generative AI stack Explore the role of vector databases in enhancing AI applications Master Python frameworks for AI development Implement Vector Search in AI applications Find out how to effectively evaluate LLM output Overcome common failures and challenges in AI development Who this book is for This book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.