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

Comprehensive guide to Large Language Models (LLMs). The document provides a detailed overview of LLMs, including their history, architecture, key examples, training methods, and applications. The guide also explores ethical considerations, practical implementation strategies, and the potential future of LLMs in various domains. The text covers topics such as fine-tuning for specific tasks, integrating LLMs into applications using APIs, and building real-world projects utilizing LLMs.

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

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

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

Concepts like the evolution of language models, neural network architectures, and transformer mechanisms. It also explores popular LLMs like GPT-3 and BERT, delves into the intricacies of training LLMs, and discusses advanced techniques like prompt engineering, few-shot learning, and multimodal capabilities. The text concludes with practical applications across various industries, real-world implementations, and future trends for LLMs.

The transformative role of generative AI in academia, focusing on its impact on both research and teaching. It offers educators, researchers, and academic leaders practical insights into how AI-generated content, models, and simulations can enhance educational practices and revolutionize research methodologies. The book delves into case studies of AI-driven advancements across disciplines, such as humanities, social sciences, and STEM fields, providing real-world applications for curriculum development, student engagement, and knowledge discovery. It also addresses the ethical considerations.

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

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

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.

Generative AI Infrastructure: Scaling and Performance Optimization" is an in-depth exploration of the technical foundations needed to deploy and scale generative AI models efficiently. The book covers the essential components of AI infrastructure, from choosing the right hardware and cloud platforms to optimizing training and inference workloads for performance. Readers will learn about distributed training techniques, GPU/TPU utilization, model compression, and techniques for reducing latency in real-time applications.

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.

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.

"LLM Evaluation: Comprehensive Insights and Practical Approaches" is a detailed guide focused on assessing the performance of large language models (LLMs). The book covers both foundational concepts and advanced techniques for evaluating LLMs across a variety of use cases, such as text generation, translation, summarization, and question-answering. It begins by explaining the significance of evaluation metrics like accuracy, precision, recall, and F1 score, while diving into more LLM-specific benchmarks, including perplexity and BLEU scores.

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.

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.

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 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.

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

he historical background of LLMs, key concepts, and various applications, such as text generation, conversational AI, and sentiment analysis. It also dives into practical considerations, including ethical considerations, model architecture, data preparation, training techniques, and deployment strategies. The document further explores advanced topics like model compression, transfer learning, and integrating LLMs with other technologies. The final chapters present case studies demonstrating real-world applications of LLMs in various industries, such as customer support, financial forecasting,