検索キーワード： 著者 "V" すべてのカテゴリー

A comprehensive guide on using Databricks, a unified data analytics platform, to master generative AI, which involves creating new content like text, images, and audio. The guide covers various aspects of generative AI, including its history, common models like GANs and VAEs, and how to implement these models in Databricks. It also discusses how to scale AI projects, evaluate model performance, and deploy them effectively. The text emphasizes the importance of ethical considerations and highlights real-world applications of generative AI in fields such as healthcare, finance, and marketing.

A comprehensive overview of how generative AI is transforming business operations, specifically within the context of NetSuite, a leading cloud-based ERP platform. The document explores the opportunities for integrating AI, particularly generative AI, to enhance decision-making, automate workflows, and improve customer experiences across various business functions. It also covers the technical aspects of integrating AI with NetSuite, including the use of APIs, data pipelines, and custom AI models.

a comprehensive overview of the mathematical foundations and applications of generative artificial intelligence (AI). It covers fundamental mathematical concepts like probability and statistics, linear algebra, and calculus, illustrating their relevance in the development and optimization of AI models. The document further explores various types of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs)

The ISO/IEC 23894:2023 standard is a guide for organizations to manage the risks associated with artificial intelligence systems. The standard provides a framework for identifying, assessing, and mitigating risks throughout the AI system lifecycle. It covers a wide range of topics, including data quality, algorithmic transparency, bias mitigation, ethical oversight, adversarial resilience, and governance

Outlines fundamental concepts of both technologies, explains how they complement each other, and presents real-world use cases in diverse domains. The guide covers deep learning fundamentals, generative adversarial networks, variational autoencoders, and transformers, while also examining blockchain technology, cryptocurrencies, decentralized finance, and non-fungible tokens. It further details practical applications in areas like AI-powered smart contracts, decentralized data storage, AI-generated NFTs, and decentralized AI marketplaces.

Explores the potential of generative AI to revolutionize telecom operations, improve customer service, and optimize network performance. It covers a wide range of use cases, including network optimization, customer service enhancement, fraud detection, content generation, and network planning. Additionally, it discusses the ethical considerations and implementation strategies for successfully adopting generative AI in the telecom sector.

Guide to understanding and implementing generative AI within organizations. It is divided into five parts, starting with an introduction to generative AI concepts, models, and applications. The second part focuses on practical steps for integrating generative AI into enterprises, covering data strategies, infrastructure, talent requirements, and transforming business models through AI.

A comprehensive guide to designing, developing, and deploying large language model (LLM) systems. It covers a wide range of topics, from the fundamentals of LLMs and their architecture to advanced deployment strategies, operationalization techniques, and ethical considerations. The document also includes practical examples, code snippets, and hands-on exercises to help readers implement LLMs in various industries, such as healthcare, finance, and education.

A comprehensive guide for developers who want to build Generative AI applications. The text explains the foundations of Generative AI and introduces AWS Bedrock as a cloud-based platform designed for building these applications. The book outlines how to choose the right Foundational Models, fine-tune them with Low-Rank Adaptation (LoRA) for specific tasks, and write effective prompts to guide the models' output. The book also explores key aspects of building a Generative AI application, such as user interface design, integration with other AWS services, and security considerations.

Explains the role of prompt engineering in guiding large language models (LLMs) to solve problems and perform tasks. The document focuses on three prompting techniques: Chain of Thought (CoT), Tree of Thought (ToT), and Self-Reflection, describing how each technique allows LLMs to reason through problems, consider multiple solutions, and analyze their own reasoning process. It then explores the use of prompt engineering in various applications such as multi-modal models, dynamic prompting, and autonomous decision-making. The document concludes with a discussion on the future of prompt engineer

A comprehensive introduction to generative AI, specifically focusing on OpenAI's GPT models and their applications, particularly ChatGPT. It covers the fundamental concepts of generative AI, the evolution of OpenAI's GPT models, the capabilities and limitations of ChatGPT, ethical considerations, and potential future directions for the field.

OpenCV, a computer vision library, with Large Language Models (LLMs), which are AI systems designed to understand and generate human language. It covers the fundamentals of both technologies, including their key features and applications. The guide then explores the building blocks for integration, focusing on data preprocessing, feature extraction, and communication between OpenCV and LLMs. It further delves into practical implementations of this integration, covering various tasks like image captioning, object detection with contextual understanding, visual question answering, and scene text

A comprehensive guide to Large Language Model (LLM) engineering, covering fundamental concepts, development practices, deployment strategies, and ethical considerations. The guide starts by introducing LLMs, their history, and various applications, then explores key NLP concepts and the Transformer architecture. The text then delves into LLM training techniques, including data collection, preprocessing, fine-tuning, and performance optimization. It also provides practical examples and hands-on exercises to illustrate various concepts and techniques. The guide further discusses advanced techniq

Explaining LLMs, their evolution, and applications in different industries. The book then dives into data preparation and management, including techniques for collecting, cleaning, and storing large datasets. It then guides the reader through building the model, focusing on model architecture design, training techniques, and hyperparameter tuning. After that, the book examines model evaluation and fine-tuning techniques, including common issues and debugging strategies.

The document "Amazon Bedrock and Generative AI.pdf" is a comprehensive guide to understanding and using Amazon Bedrock, a service designed to simplify the development and deployment of generative AI models. It covers the fundamentals of generative AI, explains how to use Bedrock to build, train, and evaluate models, and delves into advanced topics like scalable deployment, ethical considerations, and cost management. The document also includes hands-on projects and case studies to illustrate practical applications of generative AI across different industries.

This practical guide demystifies generative AI, offering a clear introduction to its underlying concepts and methods. The book covers foundational topics, such as neural networks, deep learning, and key architectures like GPT, BERT, and GANs. Readers will gain hands-on experience by building and training AI models using open-source frameworks. Detailed examples and exercises guide users through building text generation, image synthesis, and more. Whether you’re a beginner or an AI enthusiast, this guide provides a solid foundation for understanding and implementing generative AI across ...

An introduction to generative AI and LLMs, outlining their history, applications, and key concepts like tokens, embeddings, and attention mechanisms. The guide then delves into the mathematical and statistical foundations of LLMs, covering essential topics such as probability theory, linear algebra, calculus, and deep learning basics. The main focus is on practical aspects of designing and training LLMs, including data collection, data preprocessing, model architectures, training techniques, evaluation metrics, and fine-tuning. The text further explores deploying LLMs in production environment

Use of large language models (LLMs) for advanced time series analysis, focusing on how these powerful models can be used for forecasting, anomaly detection, and classification in various domains such as finance, healthcare, energy, and manufacturing. The book covers important topics related to preprocessing time series data for LLMs, adapting LLMs for specific applications, fine-tuning strategies, ethical considerations, and future trends in this emerging field.

This document, "Business Analysis with Generative AI," provides a comprehensive guide to integrating generative AI into business analysis practices. It explores various aspects of generative AI, including its models, algorithms, and tools. The document also examines practical applications of generative AI in market analysis, customer insights, process optimization, and more. It addresses ethical considerations, regulatory challenges, and future trends in the field. Finally, the document offers best practices for implementing generative AI within organizations, including strategies for building

Explaining the fundamentals of both technologies, including concepts like generative models, quantum mechanics, and quantum algorithms. The document then explores how quantum computing can be used to enhance generative AI, focusing on areas like quantum machine learning and the development of quantum generative models. It further discusses the practical implications of these technologies, such as accelerating drug discovery, optimizing supply chains, and enhancing creative content generation