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Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 42
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Book Description
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.
Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 42
Get Book Here
Book Description
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.
Author: Andrei Gheorghiu
Publisher: Packt Publishing Ltd
ISBN: 1805124404
Category : Computers
Languages : en
Pages : 368
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Book Description
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.
Author: Ben Auffarth
Publisher: Packt Publishing Ltd
ISBN: 1835088368
Category : Computers
Languages : en
Pages : 369
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Book Description
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.
Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 143
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Book Description
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.
Author: Dilyan Grigorov
Publisher: Springer Nature
ISBN:
Category :
Languages : en
Pages : 395
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Book Description
Author: Sowmya Vajjala
Publisher: O'Reilly Media
ISBN: 149205402X
Category : Computers
Languages : en
Pages : 455
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Book Description
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
Author: Bhargav Srinivasa-Desikan
Publisher: Packt Publishing Ltd
ISBN: 1788837037
Category : Computers
Languages : en
Pages : 298
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Book Description
Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms. Key Features Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms Learn deep learning techniques for text analysis Book Description Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis. What you will learn Why text analysis is important in our modern age Understand NLP terminology and get to know the Python tools and datasets Learn how to pre-process and clean textual data Convert textual data into vector space representations Using spaCy to process text Train your own NLP models for computational linguistics Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn Employ deep learning techniques for text analysis using Keras Who this book is for This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!
Author: Rachelle Palmer
Publisher: Packt Publishing Ltd
ISBN: 1836207247
Category : Computers
Languages : en
Pages : 299
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Book Description
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.
Author: Doug Farrell
Publisher: Simon and Schuster
ISBN: 1617297445
Category : Computers
Languages : en
Pages : 294
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Book Description
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.
Author: Valentina Alto
Publisher: Packt Publishing Ltd
ISBN: 1835462634
Category : Computers
Languages : en
Pages : 343
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Book Description
Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications 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.
