Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Generative AI Engineering PDF full book. Access full book title Generative AI Engineering by Anand Vemula. Download full books in PDF and EPUB format.
Author: Anand Vemula Publisher: Independently Published ISBN: Category : Computers Languages : en Pages : 0
Book Description
Generative AI Engineering: Techniques, Tools, and Applications is a comprehensive guide that delves into the fascinating world of generative artificial intelligence (AI). This book is designed for AI practitioners, researchers, and enthusiasts who want to understand the principles, methodologies, and practical implementations of generative models. The journey begins with an introduction to generative AI, exploring its history, evolution, key applications, and the ethical considerations that come with this powerful technology. The fundamentals of machine learning and deep learning are covered next, laying a solid foundation with an overview of neural networks, their architectures, and common algorithms. The book then delves into the heart of generative AI with detailed chapters on various generative models. Variational Autoencoders (VAEs) are explored first, from their mathematical foundations to implementation and real-world applications. This is followed by a deep dive into Generative Adversarial Networks (GANs), examining their architecture, training techniques, and different variants like DCGAN and StyleGAN. Autoregressive models, including RNNs, LSTMs, and Transformers, are discussed next, highlighting their training and optimization techniques. Diffusion models, with their unique approach to generating data through noise reduction, are also thoroughly explained. Advanced topics in generative AI are explored, including transfer learning, few-shot and zero-shot learning, and the integration of reinforcement learning. Hybrid models that combine different generative approaches are also discussed. Practical implementation guides provide step-by-step instructions for building, training, and deploying generative models using popular tools and libraries like TensorFlow, PyTorch, and Keras. Evaluation and metrics chapters offer insights into assessing the performance of generative models using qualitative and quantitative measures. The book also covers diverse applications and case studies, showcasing the impact of generative AI in fields such as image and text generation, music and audio synthesis, video creation, healthcare, and the creative industries. Finally, the book addresses current challenges in generative AI, including ethical and societal impacts, and outlines future research directions that promise to drive the field forward.
Author: Anand Vemula Publisher: Independently Published ISBN: Category : Computers Languages : en Pages : 0
Book Description
Generative AI Engineering: Techniques, Tools, and Applications is a comprehensive guide that delves into the fascinating world of generative artificial intelligence (AI). This book is designed for AI practitioners, researchers, and enthusiasts who want to understand the principles, methodologies, and practical implementations of generative models. The journey begins with an introduction to generative AI, exploring its history, evolution, key applications, and the ethical considerations that come with this powerful technology. The fundamentals of machine learning and deep learning are covered next, laying a solid foundation with an overview of neural networks, their architectures, and common algorithms. The book then delves into the heart of generative AI with detailed chapters on various generative models. Variational Autoencoders (VAEs) are explored first, from their mathematical foundations to implementation and real-world applications. This is followed by a deep dive into Generative Adversarial Networks (GANs), examining their architecture, training techniques, and different variants like DCGAN and StyleGAN. Autoregressive models, including RNNs, LSTMs, and Transformers, are discussed next, highlighting their training and optimization techniques. Diffusion models, with their unique approach to generating data through noise reduction, are also thoroughly explained. Advanced topics in generative AI are explored, including transfer learning, few-shot and zero-shot learning, and the integration of reinforcement learning. Hybrid models that combine different generative approaches are also discussed. Practical implementation guides provide step-by-step instructions for building, training, and deploying generative models using popular tools and libraries like TensorFlow, PyTorch, and Keras. Evaluation and metrics chapters offer insights into assessing the performance of generative models using qualitative and quantitative measures. The book also covers diverse applications and case studies, showcasing the impact of generative AI in fields such as image and text generation, music and audio synthesis, video creation, healthcare, and the creative industries. Finally, the book addresses current challenges in generative AI, including ethical and societal impacts, and outlines future research directions that promise to drive the field forward.
Author: James Phoenix Publisher: "O'Reilly Media, Inc." ISBN: 1098153405 Category : Computers Languages : en Pages : 423
Book Description
Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code
Author: Ian Khan Publisher: John Wiley & Sons ISBN: 1394243332 Category : Business & Economics Languages : en Pages : 485
Book Description
Design and use generative AI prompts that get helpful and practical results In The Quick Guide to Prompt Engineering, renowned technology futurist, management consultant, and AI thought leader Ian Khan delivers a practical and insightful discussion on taking the first steps in understanding and learning how to use generative AI. In this concise and quick start guide, you will learn how to design and use prompts to get the most out of Large Language Model generative AI applications like ChatGPT, DALL-E, Google’s Bard, and more. In the book, you’ll explore how to understand generative artificial intelligence and how to engineer prompts in a wide variety of industry use cases. You’ll also find thoughtful and illuminating case studies and hands-on exercises, as well as step-by-step guides, to get you up to speed on prompt engineering in no time at all. The book has been written for the non-technical user to take the first steps in the world of generative AI. Along with a helpful glossary of common terms, lists of useful additional reading and resources, and other resources, you’ll get: Explanations of the basics of generative artificial intelligence that help you to learn what’s going on under the hood of ChatGPT and other LLMs Stepwise guides to creating effective, efficient, and ethical prompts that help you get the most utility possible from these exciting new tools Strategies for generating text, images, video, voice, music, and other audio from various publicly available artificial intelligence tools Perfect for anyone with an interest in one of the newest and most practical technological advancements recently released to the public, The Quick Guide to Prompt Engineering is a must-read for tech enthusiasts, marketers, content creators, technical professionals, data experts, and anyone else expected to understand and use generative AI at work or at home. No previous experience is required.
Author: Anand Vemula Publisher: Anand Vemula ISBN: Category : Computers Languages : en Pages : 54
Book Description
A comprehensive guide to mastering the art and science of crafting effective prompts to unleash the full potential of generative AI. This book delves into the principles, techniques, and ethical considerations of prompt engineering, providing readers with practical insights and strategies for achieving optimal results. The book begins by exploring the fundamentals of prompt engineering, including the definition of prompts, their importance in guiding AI models, and the various types of prompts commonly used. Readers will learn about the common challenges and pitfalls of prompt engineering and gain a deeper understanding of AI models, their architecture, training processes, strengths, and limitations. Moving on to advanced topics, the book covers multi-turn prompts, contextual and sequential prompts, and leveraging model outputs to refine and enhance AI-generated content. Readers will discover domain-specific applications of prompt engineering, including creative writing, conversational AI, visual and artistic applications, and scientific and technical domains. Ethical considerations play a significant role in prompt engineering, and the book provides insights into recognizing and mitigating bias, ethical considerations, and responsible AI use. Readers will also gain practical knowledge of the tools and platforms available for prompt engineering, along with techniques for customizing and extending AI models. Testing and evaluation are essential aspects of prompt engineering, and the book offers guidance on metrics for assessing prompt performance, gathering user feedback, and conducting A/B testing and comparative analysis. Real-world case studies highlight successful applications of prompt engineering across various industries, offering valuable lessons and insights. Finally, the book explores future trends in generative AI and prompt engineering, providing readers with a glimpse into emerging technologies, predictions for the future of AI, and strategies for preparing for future developments. With its comprehensive coverage and practical insights, "Prompt Engineering for Generative AI" is an indispensable resource for AI enthusiasts, researchers, developers, and anyone looking to harness the power of generative AI through effective prompt engineering
Author: Sharma, Ramesh C. Publisher: IGI Global ISBN: Category : Education Languages : en Pages : 570
Book Description
The rise of generative Artificial Intelligence (AI) signifies a momentous stride in the evolution of Large Language Models (LLMs) within the expansive sphere of Natural Language Processing (NLP). This groundbreaking advancement ripples through numerous facets of our existence, with education, AI literacy, and curriculum enhancement emerging as focal points of transformation. Within the pages of Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation, readers embark on a journey into the heart of this transformative phenomenon. Generative AI's influence extends deeply into education, touching the lives of educators, administrators, policymakers, and learners alike. Within the pages of this book, we explore the intricate art of prompt engineering, a skill that shapes the quality of AI-generated educational content. As generative AI becomes increasingly accessible, this comprehensive volume empowers its audience, by providing them with the knowledge needed to navigate and harness the potential of this powerful tool.
Author: Chris Fregly Publisher: "O'Reilly Media, Inc." ISBN: 1098159187 Category : Languages : en Pages : 323
Book Description
Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock
Author: Anand Vemula
Author: Anand Vemula
Author: James Phoenix
Author: Ian Khan
Author: Lance Eliot
Author: Anand Vemula
Author: ARMANDO. BANACHEWICZ FANDANGO (KONRAD.)
Author: Sharma, Ramesh C.
Author: 
Author: Anh Nguyen-Duc
Author: Chris Fregly