Prasanth aruchamy (63 résultats)

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New.

Hardback. Etat : New. 1st. 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.…

Etat : New.

Hardcover. Etat : new. Hardcover. Transform the future of sustainable farming with this guide to mastering deep reinforcement learning architectures and algorithms that turn complex environmental data into precise, high-yield decisions for climate-smart agriculture. It conveys the importance of deep reinforcement learning and its technological advancements across climate-smart agriculture applications, addresses challenges related to privacy, security, and scalability of climatic and agricultural data, and explains reinforcement learning from AI and optimal control perspectives. The book explores advanced solutions such as meta learning, hierarchical learning, multi-agent learning, and imitation learning, emphasizing modern frameworks, algorithms, tools, and decision-making systems that support farmers through intelligent, data-driven applications. A machine learning method called reinforcement learning trains computers to make decisions that produce optimal outcomes by learning through trial and error. Applicable across robotics, autonomous vehicles, healthcare, finance, and agriculture, reinforcement learning plays a critical role in modern intelligent systems. This book provides a detailed analysis of climate-smart agriculture, examining farmers challenges, current technology-enabled systems, and deep reinforcement learning frameworks, algorithms, and architectures. It also addresses data privacy, security, and scalability issues in applications such as yield prediction, crop management, disease prediction, soil health monitoring, precision agriculture, and environmental monitoring. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : As New. Unread book in perfect condition.

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : new.

Buch. Etat : Neu. Neuware - 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.…

Etat : New. S. Balasubramaniam, S. Kadry, Norway A. Prasanth, R. Dhanaraj, India. Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms.

Etat : New.

Etat : As New. Unread book in perfect condition.

Hardcover. Etat : new. Hardcover. As artificial intelligence reshapes the global media landscape, AI-assisted journalism and media: opportunities and challenges provides a timely and comprehensive analysis of this technological revolution. Edited by Dr. Pham Chien Thang and Dr. A. Prasanth, this volume brings together cutting-edge research to explore how AI tools from natural language generation to predictive analytics are redefining journalistic practices. Going beyond technical theory, this book focuses on the practical relationship between human journalists and machines, demonstrating how automation can enhance rather than replace traditional reporting. It addresses critical gaps in the current literature by offering a global perspective and featuring case studies from diverse regions and countries worldwide.Key Features:Actionable Strategies: Offers practical guidelines for newsrooms to implement AI solutions, train staff, and manage technological transitions while maintaining high professional standards.Ethical & Legal Frameworks: Provides in-depth coverage of the ethical implications of AI, including transparency, accountability, and copyright considerations necessary for preserving democratic values.Balanced Analysis: Examines both the promising opportunities and the critical challenges of AI adoption, helping readers navigate issues like data quality and the changing definition of "newsworthy".Future-Proofing Media: Explores emerging trends and prepares professionals for the future of human-machine collaboration in the newsroom.Target Audience: This book is an indispensable resource for journalism professionals, newsroom managers, and media organizations seeking to navigate the AI transition. It is equally vital for journalism students, academics, and technology developers working on solutions for the news industry. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardback. Etat : New. 1st. 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.…

Etat : New.

Etat : New.

Buch. Etat : Neu. Generative AI and LLMs | Natural Language Processing and Generative Adversarial Networks | S. Balasubramaniam (u. a.) | Buch | XIV | Englisch | 2024 | Walter de Gruyter | EAN 9783111424637 | Verantwortliche Person für die EU: Walter de Gruyter GmbH, De Gruyter GmbH, Genthiner Str. 13, 10785 Berlin, productsafety[at]degruyterbrill[dot]com | Anbieter: preigu. …

Etat : New.

Etat : New. 2026. 1st Edition. hardcover. . . . . .

Etat : As New. Unread book in perfect condition.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - As artificial intelligence reshapes the global media landscape, AI-assisted journalism and media: opportunities and challenges provides a timely and comprehensive analysis of this technological revolution. Edited by Dr. Pham Chien Thang and Dr. A. Prasanth, this volume brings together cutting-edge research to explore how AI tools from natural language generation to predictive analytics are redefining journalistic practices. Going beyond technical theory, this book focuses on the practical relationship between human journalists and machines, demonstrating how automation can enhance rather than replace traditional reporting. It addresses critical gaps in the current literature by offering a global perspective and featuring case studies from diverse regions and countries worldwide.Key Features:Actionable Strategies: Offers practical guidelines for newsrooms to implement AI solutions, train staff, and manage technological transitions while maintaining high professional standards.Ethical & Legal Frameworks: Provides in-depth coverage of the ethical implications of AI, including transparency, accountability, and copyright considerations necessary for preserving democratic values.Balanced Analysis: Examines both the promising opportunities and the critical challenges of AI adoption, helping readers navigate issues like data quality and the changing definition of 'newsworthy'.Future-Proofing Media: Explores emerging trends and prepares professionals for the future of human-machine collaboration in the newsroom.Target Audience: This book is an indispensable resource for journalism professionals, newsroom managers, and media organizations seeking to navigate the AI transition. It is equally vital for journalism students, academics, and technology developers working on solutions for the news industry.…

Hardcover. Etat : Brand New. 352 pages. 6.10x0.90x9.10 inches. In Stock.

Hardcover. Etat : new. Hardcover. Transform the future of sustainable farming with this guide to mastering deep reinforcement learning architectures and algorithms that turn complex environmental data into precise, high-yield decisions for climate-smart agriculture. It conveys the importance of deep reinforcement learning and its technological advancements across climate-smart agriculture applications, addresses challenges related to privacy, security, and scalability of climatic and agricultural data, and explains reinforcement learning from AI and optimal control perspectives. The book explores advanced solutions such as meta learning, hierarchical learning, multi-agent learning, and imitation learning, emphasizing modern frameworks, algorithms, tools, and decision-making systems that support farmers through intelligent, data-driven applications. A machine learning method called reinforcement learning trains computers to make decisions that produce optimal outcomes by learning through trial and error. Applicable across robotics, autonomous vehicles, healthcare, finance, and agriculture, reinforcement learning plays a critical role in modern intelligent systems. This book provides a detailed analysis of climate-smart agriculture, examining farmers challenges, current technology-enabled systems, and deep reinforcement learning frameworks, algorithms, and architectures. It also addresses data privacy, security, and scalability issues in applications such as yield prediction, crop management, disease prediction, soil health monitoring, precision agriculture, and environmental monitoring. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Etat : New.