Mostrando resultados por autor "Anand V" en Todas las categorías

Provides a comprehensive guide to understanding, building, and deploying large language models (LLMs) in enterprise settings. It covers fundamental concepts in natural language processing (NLP), common LLM architectures like BERT, GPT, and T5, data collection and preparation techniques, model training, and fine-tuning methods. The text further explores crucial production aspects, including infrastructure optimization, security, compliance, and continuous monitoring.

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.

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)

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

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 guide to understanding the Claud LLM, a sophisticated large language model designed for text understanding and generation. It delves into Claud's technical architecture, training methods, and various applications, highlighting its capabilities in diverse domains such as healthcare, finance, and education. The text also addresses ethical considerations, including bias mitigation, privacy concerns, and responsible deployment of the model.

Generative artificial intelligence (AI), focusing on its implementation using the C++ programming language. The text covers fundamental concepts, techniques, and practical applications of generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). The sources also explain how to build neural networks, train deep learning models, and perform tasks related to natural language processing (NLP), such as text preprocessing and word embeddings.

Generative AI Evaluation: Metrics, Methods, and Best Practices" is a comprehensive resource aimed at evaluating generative AI models used in applications like text generation, image synthesis, and creative content production. It begins by explaining the unique challenges of assessing generative models, such as balancing creativity, coherence, and diversity in outputs, while avoiding mode collapse or repetitive patterns.

Defining generative AI and explaining its various applications, including text generation, image synthesis, music creation, and code generation. The book then outlines the SDLC phases, including planning, requirements gathering, design, implementation, testing, deployment, and maintenance, and explores how generative AI can be utilized within each phase to improve efficiency, accuracy, and quality. The author also discusses ethical, legal, and future considerations for integrating AI into software development, offering industry case studies and practical examples to illustrate its real-world

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.

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.

LLM Marketing: Harnessing AI to Revolutionize Customer Engagement explores the transformative power of Large Language Models (LLMs) in the marketing landscape. This book provides marketers, business leaders, and technologists with actionable insights into how AI-driven LLMs can optimize customer engagement strategies. It dives into the capabilities of LLMs in understanding customer behavior, crafting personalized content, automating responses, and delivering intelligent, real-time interactions.

A comprehensive guide to the rapidly developing field of generative artificial intelligence (AI). The document introduces the core concepts, techniques, and applications of generative AI, including its history and evolution, key terminology, and different types of generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models. The text provides practical examples, code snippets, and step-by-step instructions to help readers develop their own generative AI systems. Furthermore, the document explores advanced techniques like fine-tuning

A guide for anyone preparing for interviews about large language models (LLMs). It covers the fundamentals of LLMs and their applications, including concepts like tokenization, embeddings, neural networks, and common NLP tasks. The guide also provides sample interview questions and coding challenges, broken down into basic, intermediate, and advanced categories. The document concludes with advice for interview preparation, including study resources, mock interview tips, and strategies for addressing behavioral questions.

Explores the legal landscape surrounding the rapid development and implementation of generative AI technologies. It examines the foundational technologies powering generative AI, including machine learning, deep learning, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs). The document then dives into the legal frameworks surrounding intellectual property, data protection, and liability as they pertain to AI, outlining issues surrounding copyright, data ownership, and legal responsibility for harmful AI outputs.

Comprehensive guide to large language models (LLMs), artificial intelligence systems designed to understand and manipulate human language. It covers the history and evolution of LLMs, including key concepts like the Transformer architecture and attention mechanisms. The document then explores popular LLM models, such as GPT-3 and BERT, along with their use cases and applications in various industries, including business, finance, marketing, entertainment, and healthcare. The text further details the training process for LLMs, including data collection, preprocessing, and optimization technique

Explores the emerging field of generative artificial intelligence, which focuses on creating new content like images, music, and text. The document examines its core concepts and technologies, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models. It then details how generative AI is transforming various industries, such as healthcare, finance, manufacturing, and retail, by automating creative processes, improving efficiency, and facilitating innovation

A Comprehensive Guide to Innovation, Implementation, and Continuous Improvement," is a guide to help organizations transition to a new standard or framework known as SGP 32. The document details the key aspects of SGP 32, including its historical context, benefits, challenges, and implementation strategies. It covers areas such as resource allocation, change management, performance metrics, and best practices for achieving a successful transition. The document also explores the future outlook of SGP 32, emphasizing the importance of innovation, continuous improvement, and adapting to evolving

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.

Generative AI Projects: A Hands-On Guide" is a comprehensive resource designed for anyone eager to explore the world of generative AI through practical, real-world applications. This book is tailored to developers, AI enthusiasts, and data scientists who want to learn by doing, with a focus on creating functional generative AI solutions from scratch. It covers foundational concepts of generative models, including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and transformer-based architectures.