検索キーワード： 著者 "Anand V" すべてのカテゴリー

Covers fundamental concepts like understanding LLM architecture and the importance of prompt clarity and specificity, and dives into advanced techniques such as contextual prompting, multi-turn interactions, and fine-tuning. The guide also addresses the crucial topic of ethics in LLM development, including mitigating bias and ensuring fairness, and presents practical applications and real-world case studies of LLM implementation. Lastly, the guide discusses emerging trends in LLM development and provides resources for further learning.

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.

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.

LLM Medical: Revolutionizing Healthcare with Large Language Models" delves into the transformative impact of large language models (LLMs) on the healthcare industry. This book explores how LLMs like GPT and BERT are being used to revolutionize medical research, diagnostics, patient care, and healthcare administration. It highlights case studies in clinical decision support, medical imaging analysis, automated patient communication, and personalized treatment plans.

Vector Databases for Generative AI Applications" provides a comprehensive overview of how vector databases empower generative AI applications. It begins by explaining the core concepts of vector embeddings and vector databases, highlighting their advantages over traditional databases for storing and retrieving data based on similarity. The document then details the process of designing and implementing a vector database workflow, including data preprocessing, database selection, and integration with generative AI models. The document also discusses various applications of vector databases in t

This document is a comprehensive guide to the business applications of generative AI, a subfield of artificial intelligence that focuses on creating new content or data. It covers a wide range of topics, including the history and key technologies of generative AI, its applications in different industries like healthcare, finance, and retail, the process of building and deploying generative AI systems, and the ethical, legal, and regulatory considerations associated with its use. The document concludes by outlining the future trends of generative AI and providing a roadmap for businesses to ado

A comprehensive guide to understanding and implementing generative AI in the field of drug safety. The document explains the fundamentals of generative AI and its application in pharmacovigilance, including its potential for improving adverse event detection, risk prediction, data augmentation, and signal detection. It also examines the ethical, legal, and regulatory considerations surrounding AI in this domain

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

Explains the security considerations for generative artificial intelligence (AI), which is a type of AI capable of creating new content, such as images and text. The document examines common threats to generative AI systems, such as adversarial attacks, data poisoning, and model theft, and presents techniques to mitigate these risks, such as robust training data, adversarial training, and secure data storage. The document also explores the ethical implications of generative AI, including issues of bias and discrimination, and offers guidelines for developing and deploying AI in a responsible

Ccomprehensive guide to Gemini AI, a new multimodal generative AI framework. The text explains the architecture of Gemini and explores how it can be used for various tasks including text generation, image synthesis, and computer vision. It dives into the use of Gemini in various industries such as healthcare, content creation, and design. The document also explores ethical considerations related to Gemini AI, emphasizing responsible use, bias mitigation, and data security. Finally, the document concludes by discussing future trends in generative AI and how Gemini will play a significant role.

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.

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

Acts as a comprehensive guide to the field of Large Language Model (LLM) training, covering various aspects from the basics of natural language processing (NLP) and LLM architecture to advanced techniques like transfer learning, reinforcement learning, and multi-task learning. The book also addresses practical considerations like data collection, preprocessing, and model evaluation while discussing ethical and privacy implications. Lastly, the text includes hands-on exercises an

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

Overview of how to understand, train, and deploy large language models (LLMs), powerful AI systems capable of processing and generating human-like text. The guide begins by defining LLMs and their key concepts, then covers setting up an environment, collecting and preparing training data, selecting appropriate LLM architectures, and training the model itself. Further chapters explore how to fine-tune pre-trained LLMs for specific tasks, deploy these models for real-world applications, and evaluate their performance using various metrics

Algorithms that create new content like text, images, and music. The document explores key ethical issues like bias and fairness, transparency and explainability, privacy and data security, autonomy and control, and accountability and responsibility. It also discusses frameworks for responsible development and deployment, including guidelines, regulations, and stakeholder perspectives.

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.

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.

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.