Showing results by author "Anand V" in All Categories

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

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

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.

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.

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

Podcasts which open the doors for Better Mental Health Join my channel for audio/video consultation- https://bit.ly/PsychologyforYOU . Please DONATE We are running a Charity Program and you can donate here through Paypal - https://psycholagyclinic.blogspot.com/ . For psychology related information and videos please click this link – http://bit.ly/psychologyforall . Email : psychologyforall@rediffmail.com

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

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

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

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

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

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

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.

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 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.

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.

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 ...

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.

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.