Showing results by author "Anand" in All Categories

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.

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 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.

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.

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

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

Enhancing Creativity and Productivity explores how writers can leverage generative AI tools to amplify creativity, streamline workflows, and elevate their craft. This book provides practical insights into using AI models, such as ChatGPT, Jasper, and other natural language processing tools, to enhance brainstorming, develop plotlines, generate engaging dialogue, and refine narrative styles. It covers techniques for incorporating AI into various stages of the writing process, from ideation to editing, making it a valuable guide for both novice and experienced writers.

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.

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.

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

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.

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

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

How to use large language models (LLMs) for enterprise applications. The text covers the basics of LLM technology, setting up an LLM environment, building LLM-powered applications, and integrating LLMs with existing systems. The book also discusses ethical and responsible AI with LLMs, evaluating LLM performance, and case studies of successful LLM implementations in diverse fields like healthcare, finance, and retail. Finally, the excerpt explores emerging trends and technologies in LLM development, including multimodal models, smaller and more efficient models, and adaptive models.

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.

explores the groundbreaking advancements of the Gemini LLM, a state-of-the-art large language model. It details the model's key features, including its mastery of context, multimodal capabilities, and personalization. The text also delves into the historical development of large language models, highlighting the importance of the transformer architecture and pretraining techniques. Additionally, the document discusses the ethical considerations associated with LLMs, like bias and data privacy, as well as the potential impact of Gemini on various industries.

European Union’s (EU) regulation of artificial intelligence (AI). The document explores the rise of AI, outlining its potential benefits and challenges. It then delves into the specific details of the EU AI Act, its goals, and its risk-based approach for classifying AI systems. The Act categorizes AI systems into four risk levels, ranging from unacceptable to minimal, and establishes distinct compliance requirements for each category.

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.

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.

A comprehensive exploration of the burgeoning field of generative artificial intelligence (AI). This book delves into the ethical considerations and societal implications surrounding the development and deployment of generative AI technologies. Readers will gain insights into crucial topics such as bias and fairness, privacy concerns, transparency, and accountability. Through real-world examples, use cases, case studies, and scenarios, the book illuminates the potential of generative AI to revolutionize various domains, from natural language processing and computer vision to healthcare and bey