Generative Ai Foundations In Python

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Generative Ai Foundations In Python

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Author : Carlos Rodriguez
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-26

Generative Ai Foundations In Python written by Carlos Rodriguez 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 2024-07-26 with Computers categories.

Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorials Key Features Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation Use transformers-based LLMs and diffusion models to implement AI applications Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.What you will learn Discover the fundamentals of GenAI and its foundations in NLP Dissect foundational generative architectures including GANs, transformers, and diffusion models Find out how to fine-tune LLMs for specific NLP tasks Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs Who this book is for This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.

Generative Ai Foundations Developments And Applications

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Author : Kannan, Rajkumar
language : en
Publisher: IGI Global
Release Date : 2025-03-26

Generative Ai Foundations Developments And Applications written by Kannan, Rajkumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-26 with Computers categories.

In recent years, the field of generative artificial intelligence (AI) has witnessed remarkable advancements, transforming various domains from art and music to language and healthcare. Advanced techniques, such as conditional generation, style transfer, and unsupervised learning, showcase the cutting-edge research shaping the field. The ability of generative AI models to create novel content autonomously has sparked immense interest and innovation. Future directions provide speculations for potential breakthroughs, challenges, and opportunities for further research and innovation. Generative AI Foundations, Developments, and Applications serves as a resource to understanding generative AI across various domains including natural language processing, computer vision, and drug discovery. It explores the theoretical foundations, latest developments, and practical applications of generative AI. Covering topics such as prompt engineering, multimodal data fusion, and natural language processing, this book is an excellent resource for computer scientists, computer engineers, practitioners, professionals, researchers, scholars, academicians, and more.

Building Ai Applications With Openai Apis

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Author : Martin Yanev
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-10-04

Building Ai Applications With Openai Apis written by Martin Yanev 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 2024-10-04 with Computers categories.

Improve your app development skills by building a ChatGPT clone, code bug fixer, quiz generator, translation app, email auto-reply, PowerPoint generator, and more Key Features Transition into an expert AI developer by mastering ChatGPT concepts, including fine-tuning and integrations Gain hands-on experience through real-world projects covering a wide range of AI applications Implement payment systems in your applications by integrating the ChatGPT API with Stripe Purchase of the print or Kindle book includes a free PDF eBook Book Description Unlock the power of AI in your applications with ChatGPT with this practical guide that shows you how to seamlessly integrate OpenAI APIs into your projects, enabling you to navigate complex APIs and ensure seamless functionality with ease. This new edition is updated with key topics such as OpenAI Embeddings, which’ll help you understand the semantic relationships between words and phrases. You’ll find out how to use ChatGPT, Whisper, and DALL-E APIs through 10 AI projects using the latest OpenAI models, GPT-3.5, and GPT-4, with Visual Studio Code as the IDE. Within these projects, you’ll integrate ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt. You’ll get to grips with NLP tasks, build a ChatGPT clone, and create an AI code bug-fixing SaaS app. The chapters will also take you through speech recognition, text-to-speech capabilities, language translation, generating email replies, creating PowerPoint presentations, and fine-tuning ChatGPT, along with adding payment methods by integrating the ChatGPT API with Stripe. By the end of this book, you’ll be able to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs. What you will learn Develop a solid foundation in using the OpenAI API for NLP tasks Build, deploy, and integrate payments into various desktop and SaaS AI applications Integrate ChatGPT with frameworks such as Flask, Django, and Microsoft Office APIs Unleash your creativity by integrating DALL-E APIs to generate stunning AI art within your desktop apps Experience the power of Whisper API's speech recognition and text-to-speech features Find out how to fine-tune ChatGPT models for your specific use case Master AI embeddings to measure the relatedness of text strings Who this book is for This book is for a diverse range of professionals, including programmers, entrepreneurs, and software enthusiasts. Beginner programmers, Python developers exploring AI applications with ChatGPT, software developers integrating AI technology, and web developers creating AI-powered web applications with ChatGPT will find this book beneficial. Scholars and researchers working on AI projects with ChatGPT will also find it valuable. Basic knowledge of Python and familiarity with APIs is needed to understand the topics covered in this book.

Python Natural Language Processing Cookbook

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Author : Zhenya Antić
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-09-13

Python Natural Language Processing Cookbook written by Zhenya Antić 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 2024-09-13 with Computers categories.

Updated to include three new chapters on transformers, natural language understanding (NLU) with explainable AI, and dabbling with popular LLMs from Hugging Face and OpenAI Key Features Leverage ready-to-use recipes with the latest LLMs, including Mistral, Llama, and OpenAI models Use LLM-powered agents for custom tasks and real-world interactions Gain practical, in-depth knowledge of transformers and their role in implementing various NLP tasks with open-source and advanced LLMs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionHarness the power of Natural Language Processing (NLP) to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess. You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs. This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust in your NLP models. By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.What you will learn Understand fundamental NLP concepts along with their applications using examples in Python Classify text quickly and accurately with rule-based and supervised methods Train NER models and perform sentiment analysis to identify entities and emotions in text Explore topic modeling and text visualization to reveal themes and relationships within text Leverage Hugging Face and OpenAI LLMs to perform advanced NLP tasks Use question-answering techniques to handle both open and closed domains Apply XAI techniques to better understand your model predictions Who this book is for This updated edition of the Python Natural Language Processing Cookbook is for data scientists, machine learning engineers, and developers with a background in Python. Whether you’re looking to learn NLP techniques, extract valuable insights from textual data, or create foundational applications, this book will equip you with basic to intermediate skills. No prior NLP knowledge is necessary to get started. All you need is familiarity with basic programming principles. For seasoned developers, the updated sections offer the latest on transformers, explainable AI, and Generative AI with LLMs.

Artificial Intelligence With Python

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Author : Prateek Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-01-27

Artificial Intelligence With Python written by Prateek Joshi 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 2017-01-27 with Computers categories.

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.

Ux For Enterprise Chatgpt Solutions

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Author : Richard H. Miller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-09-06

Ux For Enterprise Chatgpt Solutions written by Richard H. Miller 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 2024-09-06 with Computers categories.

Create engaging AI experiences by mastering ChatGPT for business and leveraging user interface design practices, research methods, prompt engineering, the feeding lifecycle, and more Key Features Learn in-demand design thinking and user research techniques applicable to all conversational AI platforms Measure the quality and evaluate ChatGPT from a customer’s perspective for optimal user experience Set up and use your secure private data, documents, and materials to enhance your ChatGPT models Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany enterprises grapple with new technology, often hopping on the bandwagon only to abandon it when challenges emerge. This book is your guide to seamlessly integrating ChatGPT into enterprise solutions with a UX-centered approach. UX for Enterprise ChatGPT Solutions empowers you to master effective use case design and adapt UX guidelines through an engaging learning experience. Discover how to prepare your content for success by tailoring interactions to match your audience’s voice, style, and tone using prompt-engineering and fine-tuning. For UX professionals, this book is the key to anchoring your expertise in this evolving field. Writers, researchers, product managers, and linguists will learn to make insightful design decisions. You’ll explore use cases like ChatGPT-powered chat and recommendation engines, while uncovering the AI magic behind the scenes. The book introduces a and feeding model, enabling you to leverage feedback and monitoring to iterate and refine any Large Language Model solution. Packed with hundreds of tips and tricks, this guide will help you build a continuous improvement cycle suited for AI solutions. By the end, you’ll know how to craft powerful, accurate, responsive, and brand-consistent generative AI experiences, revolutionizing your organization’s use of ChatGPT.What you will learn Align with user needs by applying design thinking to tailor ChatGPT to meet customer expectations Harness user research to enhance chatbots and recommendation engines Track quality metrics and learn methods to evaluate and monitor ChatGPT's quality and usability Establish and maintain a uniform style and tone with prompt engineering and fine-tuning Apply proven heuristics by monitoring and assessing the UX for conversational experiences with trusted methods Refine continuously by implementing an ongoing process for chatbot and feeding Who this book is for This book is for user experience designers, product managers, and product owners of business and enterprise ChatGPT solutions who are interested in learning how to design and implement ChatGPT-4 solutions for enterprise needs. You should have a basic-to-intermediate level of understanding in UI/UX design concepts and fundamental knowledge of ChatGPT-4 and its capabilities.

Hands On Machine Learning With C

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Author : Kirill Kolodiazhnyi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-24

Hands On Machine Learning With C written by Kirill Kolodiazhnyi 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 2025-01-24 with Computers categories.

Apply supervised and unsupervised machine learning algorithms using C++ libraries, such as PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib using real-world examples and datasets Key Features Familiarize yourself with data processing, performance measuring, and model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy machine learning models to work on mobile and embedded devices Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a seasoned software engineer with several years of industry experience, this book will teach you the basics of machine learning (ML) and show you how to use C++ libraries, along with helping you create supervised and unsupervised ML models. You’ll gain hands-on experience in tuning and optimizing a model for various use cases, enabling you to efficiently select models and measure performance. The chapters cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries. You’ll also learn how to overcome production and deployment challenges on mobile platforms, and see how the ONNX model format can help you accomplish these tasks. This new edition has been updated with key topics such as sentiment analysis implementation using transfer learning and transformer-based models, as well as tracking and visualizing ML experiments with MLflow. An additional section shows you how to use Optuna for hyperparameter selection. The section on model deployment into mobile platform now includes a detailed explanation of real-time object detection for Android with C++. By the end of this C++ book, you’ll have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.What you will learn Employ key machine learning algorithms using various C++ libraries Load and pre-process different data types to suitable C++ data structures Find out how to identify the best parameters for a machine learning model Use anomaly detection for filtering user data Apply collaborative filtering to manage dynamic user preferences Utilize C++ libraries and APIs to manage model structures and parameters Implement C++ code for object detection using a modern neural network Who this book is for This book is for beginners looking to explore machine learning algorithms and techniques using C++. This book is also valuable for data analysts, scientists, and developers who want to implement machine learning models in production. Working knowledge of C++ is needed to make the most of this book.

Xgboost For Regression Predictive Modeling And Time Series Analysis

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Author : Partha Pritam Deka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-12-13

Xgboost For Regression Predictive Modeling And Time Series Analysis written by Partha Pritam Deka 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 2024-12-13 with Computers categories.

Master the art of predictive modeling with XGBoost and gain hands-on experience in building powerful regression, classification, and time series models using the XGBoost Python API Key Features Get up and running with this quick-start guide to building a classifier using XGBoost Get an easy-to-follow, in-depth explanation of the XGBoost technical paper Leverage XGBoost for time series forecasting by using moving average, frequency, and window methods Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionXGBoost offers a powerful solution for regression and time series analysis, enabling you to build accurate and efficient predictive models. In this book, the authors draw on their combined experience of 40+ years in the semiconductor industry to help you harness the full potential of XGBoost, from understanding its core concepts to implementing real-world applications. As you progress, you'll get to grips with the XGBoost algorithm, including its mathematical underpinnings and its advantages over other ensemble methods. You'll learn when to choose XGBoost over other predictive modeling techniques, and get hands-on guidance on implementing XGBoost using both the Python API and scikit-learn API. You'll also get to grips with essential techniques for time series data, including feature engineering, handling lag features, encoding techniques, and evaluating model performance. A unique aspect of this book is the chapter on model interpretability, where you'll use tools such as SHAP, LIME, ELI5, and Partial Dependence Plots (PDP) to understand your XGBoost models. Throughout the book, you’ll work through several hands-on exercises and real-world datasets. By the end of this book, you'll not only be building accurate models but will also be able to deploy and maintain them effectively, ensuring your solutions deliver real-world impact.What you will learn Build a strong, intuitive understanding of the XGBoost algorithm and its benefits Implement XGBoost using the Python API for practical applications Evaluate model performance using appropriate metrics Deploy XGBoost models into production environments Handle complex datasets and extract valuable insights Gain practical experience in feature engineering, feature selection, and categorical encoding Who this book is for This book is for data scientists, machine learning practitioners, analysts, and professionals interested in predictive modeling and time series analysis. Basic coding knowledge and familiarity with Python, GitHub, and other DevOps tools are required.

Ai Foundations Of Gpt

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Author : Jon Adams
language : en
Publisher: Green Mountain Computing
Release Date :

Ai Foundations Of Gpt written by Jon Adams and has been published by Green Mountain Computing this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

Dive into the heart of artificial intelligence with "AI Foundations of GPT," a groundbreaking book that charts the journey of Generative Pre-trained Transformers (GPT) from their conceptual inception to their role as cornerstones of modern AI applications. This meticulously crafted text serves as both a historical narrative and a forward-looking discussion, exploring the myriad ways in which GPT technology is reshaping our digital landscape. Key Features: Comprehensive Coverage: From the AI revolution to the future of GPT, each chapter is dedicated to a different facet of GPT technology, ensuring readers gain a well-rounded understanding of its complexities and capabilities. Accessible Explanations: Designed to cater to both AI aficionados and newcomers, the book explains the technical underpinnings of GPT models in an engaging and understandable manner. Future-Oriented: Offers a peek into the potential advancements and challenges that lie ahead for GPT technology, encouraging readers to ponder its implications for society and industry. Chapters: The AI Revolution: An overview of how artificial intelligence has evolved, setting the stage for the emergence of GPT. Understanding GPT: Breaks down the basics of Generative Pre-trained Transformers, explaining what they are and why they matter. The Mechanics of GPT: Delves into the technical aspects of how GPT models work, from algorithms to neural networks. Training GPT Models: Discusses the process of training GPT models, highlighting the resources and methodologies involved. Applications of GPT: Explores the diverse applications of GPT in various fields such as literature, customer service, and software development. Ethical Considerations: Examines the ethical dilemmas and considerations surrounding the use of GPT technology. The Business of GPT: Analyzes the economic landscape of GPT, including its impact on industries and business models. Limitations and Challenges: Acknowledges the limitations of current GPT models and the challenges facing their development. The Future of GPT: Speculates on the future advancements of GPT technology and its potential societal impacts. Whether you're deeply embedded in the world of AI or simply curious about the technologies shaping our future, "AI Foundations of GPT" offers a rich, insightful exploration of one of the most significant developments in artificial intelligence. Embark on this journey to understand not just the mechanics of GPT, but its profound implications on our world.

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.