AI Foundations of Large Language Models
AI Foundations of Large Language Models

Author: Jon Adams

Publisher: Green Mountain Computing

Published:

Total Pages: 137

ISBN-13:

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Dive into the fascinating world of artificial intelligence with Jon Adams' groundbreaking book, AI Foundations of Large Language Models. This comprehensive guide serves as a beacon for both beginners and enthusiasts eager to understand the intricate mechanisms behind the digital forces shaping our future. With Adams' expert narration, readers are invited to explore the evolution of language models that have transformed mere strings of code into entities capable of human-like text generation. Key Features: In-depth Exploration: From the initial emergence to the sophisticated development of Large Language Models (LLMs), this book covers it all. Technical Insights: Understand the foundational technology, including neural networks, transformers, and attention mechanisms, that powers LLMs. Practical Applications: Discover how LLMs are being utilized in industry and research, paving the way for future innovations. Ethical Considerations: Engage with the critical discussions surrounding the ethics of LLM development and deployment. Chapters Include: The Emergence of Language Models: An introduction to the genesis of LLMs and their significance. Foundations of Neural Networks: Delve into the neural underpinnings that make it all possible. Transformers and Attention Mechanisms: Unpack the mechanisms that enhance LLM efficiency and accuracy. Training Large Language Models: A guide through the complexities of LLM training processes. Understanding LLMs Text Generation: Insights into how LLMs generate text that rivals human writing. Natural Language Understanding: Explore the advancements in LLMs' comprehension capabilities. Ethics and LLMs: A critical look at the ethical landscape of LLM technology. LLMs in Industry and Research: Real-world applications and the impact of LLMs across various sectors. The Future of Large Language Models: Speculations and predictions on the trajectory of LLM advancements. Whether you're a student, professional, or simply an AI enthusiast, AI Foundations of Large Language Models by Jon Adams offers a riveting narrative filled with insights and foresights. Equip yourself with the knowledge to navigate the burgeoning world of LLMs and appreciate their potential to redefine our technological landscape. Join us on this enlightening journey through the annals of artificial intelligence, where the future of digital communication and creativity awaits.

Rebooting AI
Rebooting AI

Author: Gary Marcus

Publisher: Vintage

Published: 2019-09-10

Total Pages: 288

ISBN-13: 1524748269

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Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust artificial intelligence. Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence. The real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors' offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better.

Generative AI Foundations in Python
Generative AI Foundations in Python

Author: Carlos Rodriguez

Publisher: Packt Publishing Ltd

Published: 2024-07-26

Total Pages: 190

ISBN-13: 1835464912

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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.

Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges
Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges

Author: I. Tiddi

Publisher: IOS Press

Published: 2020-05-06

Total Pages: 314

ISBN-13: 1643680811

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The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.

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

Artificial Intelligence
Artificial Intelligence

Author: David L. Poole

Publisher: Cambridge University Press

Published: 2017-09-25

Total Pages: 821

ISBN-13: 110719539X

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Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.

Generative AI for Cloud Solutions
Generative AI for Cloud Solutions

Author: Paul Singh

Publisher: Packt Publishing Ltd

Published: 2024-04-22

Total Pages: 301

ISBN-13: 1835080162

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Explore Generative AI, the engine behind ChatGPT, and delve into topics like LLM-infused frameworks, autonomous agents, and responsible innovation, to gain valuable insights into the future of AI Key Features Gain foundational GenAI knowledge and understand how to scale GenAI/ChatGPT in the cloud Understand advanced techniques for customizing LLMs for organizations via fine-tuning, prompt engineering, and responsible AI Peek into the future to explore emerging trends like multimodal AI and autonomous agents Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGenerative artificial intelligence technologies and services, including ChatGPT, are transforming our work, life, and communication landscapes. To thrive in this new era, harnessing the full potential of these technologies is crucial. Generative AI for Cloud Solutions is a comprehensive guide to understanding and using Generative AI within cloud platforms. This book covers the basics of cloud computing and Generative AI/ChatGPT, addressing scaling strategies and security concerns. With its help, you’ll be able to apply responsible AI practices and other methods such as fine-tuning, RAG, autonomous agents, LLMOps, and Assistants APIs. As you progress, you’ll learn how to design and implement secure and scalable ChatGPT solutions on the cloud, while also gaining insights into the foundations of building conversational AI, such as chatbots. This process will help you customize your AI applications to suit your specific requirements. By the end of this book, you’ll have gained a solid understanding of the capabilities of Generative AI and cloud computing, empowering you to develop efficient and ethical AI solutions for a variety of applications and services.What you will learn Get started with the essentials of generative AI, LLMs, and ChatGPT, and understand how they function together Understand how we started applying NLP to concepts like transformers Grasp the process of fine-tuning and developing apps based on RAG Explore effective prompt engineering strategies Acquire insights into the app development frameworks and lifecycles of LLMs, including important aspects of LLMOps, autonomous agents, and Assistants APIs Discover how to scale and secure GenAI systems, while understanding the principles of responsible AI Who this book is for This artificial intelligence book is for aspiring cloud architects, data analysts, cloud developers, data scientists, AI researchers, technical business leaders, and technology evangelists looking to understanding the interplay between GenAI and cloud computing. Some chapters provide a broad overview of GenAI, which are suitable for readers with basic to no prior AI experience, aspiring to harness AI's potential. Other chapters delve into technical concepts that require intermediate data and AI skills. A basic understanding of a cloud ecosystem is required to get the most out of this book.

Microsoft Azure AI Fundamentals AI-900 Exam Guide
Microsoft Azure AI Fundamentals AI-900 Exam Guide

Author: Aaron Guilmette

Publisher: Packt Publishing Ltd

Published: 2024-05-31

Total Pages: 288

ISBN-13: 1835885675

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Get ready to pass the certification exam on your first attempt by gaining actionable insights into AI concepts, ML techniques, and Azure AI services covered in the latest AI-900 exam syllabus from two industry experts Key Features Discover Azure AI services, including computer vision, Auto ML, NLP, and OpenAI Explore AI use cases, such as image identification, chatbots, and more Work through 145 practice questions under chapter-end self-assessments and mock exams Purchase of this book unlocks access to web-based exam prep resources, including mock exams, flashcards, and exam tips Book Description The AI-900 exam helps you take your first step into an AI-shaped future. Regardless of your technical background, this book will help you test your understanding of the key AI-related topics and tools used to develop AI solutions in Azure cloud. This exam guide focuses on AI workloads, including natural language processing (NLP) and large language models (LLMs). You'll explore Microsoft's responsible AI principles like safety and accountability. Then, you'll cover the basics of machine learning (ML), including classification and deep learning, and learn how to use training and validation datasets with Azure ML. Using Azure AI Vision, face detection, and Video Indexer services, you'll get up to speed with computer vision-related topics like image classification, object detection, and facial detection. Later chapters cover NLP features such as key phrase extraction, sentiment analysis, and speech processing using Azure AI Language, speech, and translator services. The book also guides you through identifying GenAI models and leveraging Azure OpenAI Service for content generation. At the end of each chapter, you'll find chapter review questions with answers, provided as an online resource. By the end of this exam guide, you'll be able to work with AI solutions in Azure and pass the AI-900 exam using the online exam prep resources. What you will learn Discover various types of artificial intelligence (AI)workloads and services in Azure Cover Microsoft's guiding principles for responsible AI development and use Understand the fundamental principles of how AI and machine learning work Explore how AI models can recognize content in images and documents Gain insights into the features and use cases for natural language processing Explore the capabilities of generative AI services Who this book is for Whether you're a cloud engineer, software developer, an aspiring data scientist, or simply interested in learning AI/ML concepts and capabilities on Azure, this book is for you. The book also serves as a foundation for those looking to attempt more advanced AI and data science-related certification exams (e.g. Microsoft Certified: Azure AI Engineer Associate). Although no experience in data science and software engineering is required, basic knowledge of cloud concepts and client-server applications is assumed.

Artificial Intelligence with Python
Artificial Intelligence with Python

Author: Prateek Joshi

Publisher: Packt Publishing Ltd

Published: 2017-01-27

Total Pages: 437

ISBN-13: 1786469677

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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.

Zero to AI
Zero to AI

Author: Nicolò Valigi

Publisher: Manning

Published: 2020-05-19

Total Pages: 262

ISBN-13: 1617296066

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Summary How can artificial intelligence transform your business? In Zero to AI, you’ll explore a variety of practical AI applications you can use to improve customer experiences, optimize marketing, help you cut costs, and more. In this engaging guide written for business leaders and technology pros alike, authors and AI experts Nicolò Valigi and Gianluca Mauro use fascinating projects, hands-on activities, and real-world explanations to make it clear how your business can benefit from AI. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology There’s no doubt that artificial intelligence has made some impressive headlines recently, from besting chess and Go grand masters to producing uncanny deep fakes that blur the lines of reality. But what can AI do for you? If you want to understand how AI will impact your business before you invest your time and money, this book is for you. About the book Zero to AI uses clear examples and jargon-free explanations to show the practical benefits of AI. Each chapter explores a real-world case study demonstrating how companies like Google and Netflix use AI to shape their industries. You begin at the beginning, with a primer on core AI concepts and realistic business outcomes. To help you prepare for the transition, the book breaks down a successful AI implementation, including advice on hiring the right team and making decisions about resources, risks, and costs. What's inside Identifying where AI can help your organization Designing an AI strategy Evaluating project scope and business impact Using AI to boost conversion rates, curate content, and analyze feedback Understanding how modern AI works and what it can/can’t do About the reader For anyone who wants to gain an understanding of practical artificial intelligence and learn how to design and develop projects with high business impact. About the author Gianluca Mauro and Nicolò Valigi are the cofounders of AI Academy, a company specializing in AI trainings and consulting. Table of Contents: 1. An introduction to artificial intelligence PART 1 - UNDERSTANDING AI 2. Artificial intelligence for core business data 3. AI for sales and marketing 4. AI for media 5. AI for natural language 6. AI for content curation and community building PART 2 - BUILDING AI 7. Ready—finding AI opportunities 8. Set—preparing data, technology, and people 9. Go—AI implementation strategy 10. What lies ahead