Artificial Intelligence And Large Language Models
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Generative Ai And Llms
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Author : S. Balasubramaniam
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-09-23
Generative Ai And Llms written by S. Balasubramaniam and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-23 with Computers categories.
Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.
Artificial Intelligence
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Author : Leonidas Deligiannidis
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-08-05
Artificial Intelligence written by Leonidas Deligiannidis and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-05 with Computers categories.
Artificial Intelligence (AI) revolves around creating and utilizing intelligent machines through science and engineering. This book delves into the theory and practical applications of computer science methods that incorporate AI across many domains. It covers techniques such as Machine Learning (ML), Convolutional Neural Networks (CNN), Deep Learning (DL), and Large Language Models (LLM) to tackle complex issues and overcome various challenges.
Large Language Models A Deep Dive
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Author : Uday Kamath
language : en
Publisher: Springer Nature
Release Date : 2024-08-20
Large Language Models A Deep Dive written by Uday Kamath and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-20 with Computers categories.
Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs—their intricate architecture, underlying algorithms, and ethical considerations—require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs. Key Features: Over 100 techniques and state-of-the-art methods, including pre-training, prompt-based tuning, instruction tuning, parameter-efficient and compute-efficient fine-tuning, end-user prompt engineering, and building and optimizing Retrieval-Augmented Generation systems, along with strategies for aligning LLMs with human values using reinforcement learning Over 200 datasets compiled in one place, covering everything from pre- training to multimodal tuning, providing a robust foundation for diverse LLM applications Over 50 strategies to address key ethical issues such as hallucination, toxicity, bias, fairness, and privacy. Gain comprehensive methods for measuring, evaluating, and mitigating these challenges to ensure responsible LLM deployment Over 200 benchmarks covering LLM performance across various tasks, ethical considerations, multimodal applications, and more than 50 evaluation metrics for the LLM lifecycle Nine detailed tutorials that guide readers through pre-training, fine- tuning, alignment tuning, bias mitigation, multimodal training, and deploying large language models using tools and libraries compatible with Google Colab, ensuring practical application of theoretical concepts Over 100 practical tips for data scientists and practitioners, offering implementation details, tricks, and tools to successfully navigate the LLM life- cycle and accomplish tasks efficiently
Artificial Intelligence And Large Language Models
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Author : Kutub Thakur
language : en
Publisher: CRC Press
Release Date : 2024-07-12
Artificial Intelligence And Large Language Models written by Kutub Thakur and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-12 with Computers categories.
Having been catapulted into public discourse in the last few years, this book serves as an in-depth exploration of the ever-evolving domain of artificial intelligence (AI), large language models, and ChatGPT. It provides a meticulous and thorough analysis of AI, ChatGPT technology, and their prospective trajectories given the current trend, in addition to tracing the significant advancements that have materialized over time. Key Features: Discusses the fundamentals of AI for general readers Introduces readers to the ChatGPT chatbot and how it works Covers natural language processing (NLP), the foundational building block of ChatGPT Introduces readers to the deep learning transformer architecture Covers the fundamentals of ChatGPT training for practitioners Illustrated and organized in an accessible manner, this textbook contains particular appeal to students and course convenors at the undergraduate and graduate level, as well as a reference source for general readers.
Introduction To Artificial Intelligence
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Author : Wolfgang Ertel
language : en
Publisher: Springer
Release Date : 2018-01-18
Introduction To Artificial Intelligence written by Wolfgang Ertel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-18 with Computers categories.
This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning. Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art (NEW); examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayes’ theorem and its relevance in everyday life (NEW); discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search (NEW); includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation (NEW). Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.
Artificial Intelligence Theory And Applications
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Author : Harish Sharma
language : en
Publisher: Springer Nature
Release Date : 2025-06-24
Artificial Intelligence Theory And Applications written by Harish Sharma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-24 with Computers categories.
This book features a collection of high-quality research papers presented at International Conference on Artificial Intelligence: Theory and Applications (AITA 2024), held during 9–10 August 2024 in Bengaluru, India. The book is divided into two volumes and presents original research and review papers related to artificial intelligence and its applications in various domains including health care, finance, transportation, education, and many more.
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
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
Algorithms In Advanced Artificial Intelligence
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Author : R. N. V. Jagan Mohan
language : en
Publisher: CRC Press
Release Date : 2024-07-08
Algorithms In Advanced Artificial Intelligence written by R. N. V. Jagan Mohan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-08 with Computers categories.
The most common form of severe dementia, Alzheimer’s disease (AD), is a cumulative neurological disorder because of the degradation and death of nerve cells in the brain tissue, intelligence steadily declines and most of its activities are compromised in AD. Before diving into the level of AD diagnosis, it is essential to highlight the fundamental differences between conventional machine learning (ML) and deep learning (DL). This work covers a number of photo-preprocessing approaches that aid in learning because image processing is essential for the diagnosis of AD. The most crucial kind of neural network for computer vision used in medical image processing is called a Convolutional Neural Network (CNN). The proposed study will consider facial characteristics, including expressions and eye movements using the diffusion model, as part of CNN’s meticulous approach to Alzheimer’s diagnosis. Convolutional neural networks were used in an effort to sense Alzheimer’s disease in its early stages using a big collection of pictures of facial expressions.
The Age Of Generative Artificial Intelligence
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Author : Söz Tuana DEMİR
language : en
Publisher: İzmir Akademi Derneği
Release Date : 2025-07-16
The Age Of Generative Artificial Intelligence written by Söz Tuana DEMİR and has been published by İzmir Akademi Derneği this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-16 with Computers categories.
In recent years, the emergence of generative artificial intelligence has reshaped the boundaries of what machines can create, understand, and transform. As editors of the book titled "The Age of Generative Artificial Intelligence", we present this volume to offer a comprehensive and critical view of these advancements. This book brings together eight original chapters that reflect the multifaceted nature of this field and its impact across industries. The opening chapter presents a holistic examination of transformer-based models, covering their underlying architecture, practical applications, and the ethical considerations they raise. In the second chapter, readers explore how AI tools, especially large language models, are being integrated into primary health care, improving service delivery and patient outcomes. Our third chapter turns to the digital media landscape, analyzing how AI, particularly generative models, is transforming content production, audience engagement, and media personalization. From automated video and audio processing to dynamic content generation using tools like ChatGPT and Writesonic, this chapter highlights both the efficiencies and the ethical dilemmas emerging in the age of AI-powered media. In the fourth chapter, the creative potential of text-to-video generation is explored through models such as OpenAI’s Sora, Stable Diffusion, and Lumiere, showcasing the rapid evolution of multimodal generative capabilities. Chapter five focuses on the intersection of raw data and intelligent query generation, emphasizing the critical role of human validation to ensure reliability in AI-driven workflows. Chapter six addresses the increasing challenge of detecting AI-generated content. It offers a detailed evaluation of the current landscape of detector technologies, highlighting their limitations and the implications for digital trust. The seventh chapter delves into the complexities of agentic AI, exploring the risks of autonomous behavior in artificial systems and raising essential questions of control, responsibility, and accountability. Finally, the eighth chapter proposes a structured taxonomy of risks related to model deployment and data integrity, equipping readers with a practical framework to navigate the threats associated with widespread AI adoption. Published by İzmir Akademi Derneği (Izmir Association Academy) as part of the Future Tech Horizon series, this book seeks to inform, challenge, and inspire all who are engaged with the present and future of generative technologies.
Artificial Intelligence
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Author : Leonidas Deligiannidis
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-08-05
Artificial Intelligence written by Leonidas Deligiannidis and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-05 with Computers categories.
Artificial Intelligence (AI) revolves around creating and utilizing intelligent machines through science and engineering. This book delves into the theory and practical applications of computer science methods that incorporate AI across many domains. It covers techniques such as Machine Learning (ML), Convolutional Neural Networks (CNN), Deep Learning (DL), and Large Language Models (LLM) to tackle complex issues and overcome various challenges.