Quick Start Guide To Large Language Models

Author : Sinan Ozdemir
Publisher : Addison-Wesley Professional
Page : 0 pages
File Size : 53,5 Mb
Release : 2023-10-20
Category : Electronic
ISBN : 0138199191

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Quick Start Guide to Large Language Models by Sinan Ozdemir Pdf

The advancement of Large Language Models (LLMs) has revolutionized the field of Natural Language Processing in recent years. Models like BERT, T5, and ChatGPT have demonstrated unprecedented performance on a wide range of NLP tasks, from text classification to machine translation. Despite their impressive performance, the use of LLMs remains challenging for many practitioners. The sheer size of these models, combined with the lack of understanding of their inner workings, has made it difficult for practitioners to effectively use and optimize these models for their specific needs. Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs is a practical guide to the use of LLMs in NLP. It provides an overview of the key concepts and techniques used in LLMs and explains how these models work and how they can be used for various NLP tasks. The book also covers advanced topics, such as fine-tuning, alignment, and information retrieval while providing practical tips and tricks for training and optimizing LLMs for specific NLP tasks. This work addresses a wide range of topics in the field of Large Language Models, including the basics of LLMs, launching an application with proprietary models, fine-tuning GPT3 with custom examples, prompt engineering, building a recommendation engine, combining Transformers, and deploying custom LLMs to the cloud. It offers an in-depth look at the various concepts, techniques, and tools used in the field of Large Language Models. Topics covered: Coding with Large Language Models (LLMs) Overview of using proprietary models OpenAI, Embeddings, GPT3, and ChatGPT Vector databases and building a neural/semantic information retrieval system Fine-tuning GPT3 with custom examples Prompt engineering with GPT3 and ChatGPT Advanced prompt engineering techniques Building a recommendation engine Combining Transformers Deploying custom LLMs to the cloud

Author : Sinan Ozdemir
Publisher : Addison-Wesley Professional
Page : 428 pages
File Size : 55,9 Mb
Release : 2023-09-20
Category : Computers
ISBN : 9780138199333

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Quick Start Guide to Large Language Models by Sinan Ozdemir Pdf

The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. You'll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generation Master advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data Construct and fine-tune multimodal Transformer architectures using opensource LLMs Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind "By balancing the potential of both open- and closed-source models, Quick Start Guide to Large Language Models stands as a comprehensive guide to understanding and using LLMs, bridging the gap between theoretical concepts and practical application." --Giada Pistilli, Principal Ethicist at HuggingFace "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Author : Jon Adams
Publisher : Green Mountain Computing
Page : 137 pages
File Size : 46,8 Mb
Release : 2024-07-01
Category : Computers
ISBN : 8210379456XXX

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AI Foundations of Large Language Models by Jon Adams Pdf

Dive into the fascinating world of artificial intelligence with Jon Adams' groundbreaking book, AI Foundations of Large Language Models. This comprehensive guide serves as a beacon for both beginners and enthusiasts eager to understand the intricate mechanisms behind the digital forces shaping our future. With Adams' expert narration, readers are invited to explore the evolution of language models that have transformed mere strings of code into entities capable of human-like text generation. Key Features: In-depth Exploration: From the initial emergence to the sophisticated development of Large Language Models (LLMs), this book covers it all. Technical Insights: Understand the foundational technology, including neural networks, transformers, and attention mechanisms, that powers LLMs. Practical Applications: Discover how LLMs are being utilized in industry and research, paving the way for future innovations. Ethical Considerations: Engage with the critical discussions surrounding the ethics of LLM development and deployment. Chapters Include: The Emergence of Language Models: An introduction to the genesis of LLMs and their significance. Foundations of Neural Networks: Delve into the neural underpinnings that make it all possible. Transformers and Attention Mechanisms: Unpack the mechanisms that enhance LLM efficiency and accuracy. Training Large Language Models: A guide through the complexities of LLM training processes. Understanding LLMs Text Generation: Insights into how LLMs generate text that rivals human writing. Natural Language Understanding: Explore the advancements in LLMs' comprehension capabilities. Ethics and LLMs: A critical look at the ethical landscape of LLM technology. LLMs in Industry and Research: Real-world applications and the impact of LLMs across various sectors. The Future of Large Language Models: Speculations and predictions on the trajectory of LLM advancements. Whether you're a student, professional, or simply an AI enthusiast, AI Foundations of Large Language Models by Jon Adams offers a riveting narrative filled with insights and foresights. Equip yourself with the knowledge to navigate the burgeoning world of LLMs and appreciate their potential to redefine our technological landscape. Join us on this enlightening journey through the annals of artificial intelligence, where the future of digital communication and creativity awaits.

Author : Masaaki Kurosu
Publisher : Springer Nature
Page : 438 pages
File Size : 52,7 Mb
Release : 2024-07-01
Category : Electronic
ISBN : 9783031604058

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Human-Computer Interaction by Masaaki Kurosu Pdf

Author : Aleksandra Przegalinska,Tamilla Triantoro
Publisher : CRC Press
Page : 169 pages
File Size : 40,6 Mb
Release : 2024-06-17
Category : Technology & Engineering
ISBN : 9781040031087

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Converging Minds by Aleksandra Przegalinska,Tamilla Triantoro Pdf

This groundbreaking book explores the power of collaborative AI in amplifying human creativity and expertise. Written by two seasoned experts in data analytics, AI, and machine learning, the book offers a comprehensive overview of the creative process behind AI-powered content generation. It takes the reader through a unique collaborative process between human authors and various AI-based topic experts, created, prompted, and fine-tuned by the authors. This book features a comprehensive list of prompts that readers can use to create their own ChatGPT-powered topic experts. By following these expertly crafted prompts, individuals and businesses alike can harness the power of AI, tailoring it to their specific needs and fostering a fruitful collaboration between humans and machines. With real-world use cases and deep insights into the foundations of generative AI, the book showcases how humans and machines can work together to achieve better business outcomes and tackle complex challenges. Social and ethical implications of collaborative AI are covered and how it may impact the future of work and employment. Through reading the book, readers will gain a deep understanding of the latest advancements in AI and how they can shape our world. Converging Minds: The Creative Potential of Collaborative AI is essential reading for anyone interested in the transformative potential of AI-powered content generation and human-AI collaboration. It will appeal to data scientists, machine learning architects, prompt engineers, general computer scientists, and engineers in the fields of generative AI and deep learning. Chapter 1 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons [Attribution- No Derivatives (CC-BY -ND)] 4.0 license.

Author : Nirant Kasliwal
Publisher : Packt Publishing Ltd
Page : 177 pages
File Size : 50,8 Mb
Release : 2018-11-30
Category : Computers
ISBN : 9781788994101

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Natural Language Processing with Python Quick Start Guide by Nirant Kasliwal Pdf

Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key FeaturesA no-math, code-driven programmer’s guide to text processing and NLPGet state of the art results with modern tooling across linguistics, text vectors and machine learningFundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorchBook Description NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learnUnderstand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpusWork with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clusteringDeep Learning in NLP using PyTorch with a code-driven introduction to PyTorchUsing an NLP project management Framework for estimating timelines and organizing your project into stagesHack and build a simple chatbot application in 30 minutesDeploy an NLP or machine learning application using Flask as RESTFUL APIsWho this book is for Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.

Author : Joydeep Bhattacharjee
Publisher : Packt Publishing Ltd
Page : 183 pages
File Size : 43,5 Mb
Release : 2018-07-26
Category : Computers
ISBN : 9781789136715

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fastText Quick Start Guide by Joydeep Bhattacharjee Pdf

Perform efficient fast text representation and classification with Facebook's fastText library Key Features Introduction to Facebook's fastText library for NLP Perform efficient word representations, sentence classification, vector representation Build better, more scalable solutions for text representation and classification Book Description Facebook's fastText library handles text representation and classification, used for Natural Language Processing (NLP). Most organizations have to deal with enormous amounts of text data on a daily basis, and gaining efficient data insights requires powerful NLP tools such as fastText. This book is your ideal introduction to fastText. You will learn how to create fastText models from the command line, without the need for complicated code. You will explore the algorithms that fastText is built on and how to use them for word representation and text classification. Next, you will use fastText in conjunction with other popular libraries and frameworks such as Keras, TensorFlow, and PyTorch. Finally, you will deploy fastText models to mobile devices. By the end of this book, you will have all the required knowledge to use fastText in your own applications at work or in projects. What you will learn Create models using the default command line options in fastText Understand the algorithms used in fastText to create word vectors Combine command line text transformation capabilities and the fastText library to implement a training, validation, and prediction pipeline Explore word representation and sentence classification using fastText Use Gensim and spaCy to load the vectors, transform, lemmatize, and perform other NLP tasks efficiently Develop a fastText NLP classifier using popular frameworks, such as Keras, Tensorflow, and PyTorch Who this book is for This book is for data analysts, data scientists, and machine learning developers who want to perform efficient word representation and sentence classification using Facebook's fastText library. Basic knowledge of Python programming is required.

Author : Sinan Ozdemir
Publisher : Addison-Wesley Professional
Page : 0 pages
File Size : 54,8 Mb
Release : 2024-10
Category : Computers
ISBN : 0135346568

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Quick Start Guide to Large Language Models by Sinan Ozdemir Pdf

Author : Raj Arun R
Publisher : Orange Education Pvt Ltd
Page : 547 pages
File Size : 53,9 Mb
Release : 2024-04-12
Category : Computers
ISBN : 9788197081828

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Mastering Large Language Models with Python by Raj Arun R Pdf

A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index

Author : Singh, Abhishek,Rajput, Vishnu D.,Ghazaryan, Karen,Gupta, Santosh Kumar,Minkina, Tatiana
Publisher : IGI Global
Page : 305 pages
File Size : 40,5 Mb
Release : 2023-08-07
Category : Technology & Engineering
ISBN : 9781668472347

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Nanopriming Approach to Sustainable Agriculture by Singh, Abhishek,Rajput, Vishnu D.,Ghazaryan, Karen,Gupta, Santosh Kumar,Minkina, Tatiana Pdf

The challenges posed by climate change have had a significant impact on global food security, with crop yields negatively affected by abiotic and biotic stresses. The book Nanopriming Approach to Sustainable Agriculture offers a promising solution to this problem, providing a sustainable technology that ensures the growth and development of healthy plants in adverse conditions. By using nanoparticles to enhance seed germination and growth, nanopriming establishes pre-resistance against diseases and stresses, thereby reducing the need for pesticides and fertilizers. This reduction in the use of harmful chemicals not only benefits the environment but also helps increase the income of farmers worldwide. This edited book offers a comprehensive overview of the latest research and development in nanotechnology for agriculture, covering topics such as crop production, protection, soil fertility improvement, and crop improvement. With each chapter focusing on a specific topic, the book is a valuable resource for students, researchers, and professors seeking to stay up to date with the latest advances in nanotechnology. The book is especially relevant for those interested in precision farming, and it highlights the potential of nanopriming as an effective tool for sustainable agriculture. Topics covered include seed priming with zinc oxide and silver nanoparticles, myco-synthesized nanoparticles as seed priming agents, and nanopriming for alleviating heavy metal toxicity in plants. Co-edited by leading researchers in the field, this book is designed to be a valuable asset for university courses and provides updated knowledge and applications in agriculture sciences. Overall, this book offers a comprehensive overview of the latest research in nanotechnology for agriculture and its potential to revolutionize sustainable agriculture practices worldwide.

Author : Sebastian Raschka,Yuxi (Hayden) Liu,Vahid Mirjalili
Publisher : Packt Publishing Ltd
Page : 775 pages
File Size : 50,6 Mb
Release : 2022-02-25
Category : Computers
ISBN : 9781801816380

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Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka,Yuxi (Hayden) Liu,Vahid Mirjalili Pdf

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Author : James Chen
Publisher : James Chen
Page : 300 pages
File Size : 42,8 Mb
Release : 2024-04-25
Category : Computers
ISBN : 9781738908462

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Demystifying Large Language Models by James Chen Pdf

This book is a comprehensive guide aiming to demystify the world of transformers -- the architecture that powers Large Language Models (LLMs) like GPT and BERT. From PyTorch basics and mathematical foundations to implementing a Transformer from scratch, you'll gain a deep understanding of the inner workings of these models. That's just the beginning. Get ready to dive into the realm of pre-training your own Transformer from scratch, unlocking the power of transfer learning to fine-tune LLMs for your specific use cases, exploring advanced techniques like PEFT (Prompting for Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation) for fine-tuning, as well as RLHF (Reinforcement Learning with Human Feedback) for detoxifying LLMs to make them aligned with human values and ethical norms. Step into the deployment of LLMs, delivering these state-of-the-art language models into the real-world, whether integrating them into cloud platforms or optimizing them for edge devices, this section ensures you're equipped with the know-how to bring your AI solutions to life. Whether you're a seasoned AI practitioner, a data scientist, or a curious developer eager to advance your knowledge on the powerful LLMs, this book is your ultimate guide to mastering these cutting-edge models. By translating convoluted concepts into understandable explanations and offering a practical hands-on approach, this treasure trove of knowledge is invaluable to both aspiring beginners and seasoned professionals. Table of Contents 1. INTRODUCTION 1.1 What is AI, ML, DL, Generative AI and Large Language Model 1.2 Lifecycle of Large Language Models 1.3 Whom This Book Is For 1.4 How This Book Is Organized 1.5 Source Code and Resources 2. PYTORCH BASICS AND MATH FUNDAMENTALS 2.1 Tensor and Vector 2.2 Tensor and Matrix 2.3 Dot Product 2.4 Softmax 2.5 Cross Entropy 2.6 GPU Support 2.7 Linear Transformation 2.8 Embedding 2.9 Neural Network 2.10 Bigram and N-gram Models 2.11 Greedy, Random Sampling and Beam 2.12 Rank of Matrices 2.13 Singular Value Decomposition (SVD) 2.14 Conclusion 3. TRANSFORMER 3.1 Dataset and Tokenization 3.2 Embedding 3.3 Positional Encoding 3.4 Layer Normalization 3.5 Feed Forward 3.6 Scaled Dot-Product Attention 3.7 Mask 3.8 Multi-Head Attention 3.9 Encoder Layer and Encoder 3.10 Decoder Layer and Decoder 3.11 Transformer 3.12 Training 3.13 Inference 3.14 Conclusion 4. PRE-TRAINING 4.1 Machine Translation 4.2 Dataset and Tokenization 4.3 Load Data in Batch 4.4 Pre-Training nn.Transformer Model 4.5 Inference 4.6 Popular Large Language Models 4.7 Computational Resources 4.8 Prompt Engineering and In-context Learning (ICL) 4.9 Prompt Engineering on FLAN-T5 4.10 Pipelines 4.11 Conclusion 5. FINE-TUNING 5.1 Fine-Tuning 5.2 Parameter Efficient Fine-tuning (PEFT) 5.3 Low-Rank Adaptation (LoRA) 5.4 Adapter 5.5 Prompt Tuning 5.6 Evaluation 5.7 Reinforcement Learning 5.8 Reinforcement Learning Human Feedback (RLHF) 5.9 Implementation of RLHF 5.10 Conclusion 6. DEPLOYMENT OF LLMS 6.1 Challenges and Considerations 6.2 Pre-Deployment Optimization 6.3 Security and Privacy 6.4 Deployment Architectures 6.5 Scalability and Load Balancing 6.6 Compliance and Ethics Review 6.7 Model Versioning and Updates 6.8 LLM-Powered Applications 6.9 Vector Database 6.10 LangChain 6.11 Chatbot, Example of LLM-Powered Application 6.12 WebUI, Example of LLM-Power Application 6.13 Future Trends and Challenges 6.14 Conclusion REFERENCES ABOUT THE AUTHOR

Author : Elmoudden, Sanae,Wrench, Jason S.
Publisher : IGI Global
Page : 419 pages
File Size : 53,5 Mb
Release : 2024-02-12
Category : Education
ISBN : 9798369308325

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The Role of Generative AI in the Communication Classroom by Elmoudden, Sanae,Wrench, Jason S. Pdf

In an era marked by the rapid integration of Artificial Intelligence (AI) into our lives, the discourse surrounding its implications has intensified. The Role of Generative AI in the Communication Classroom is a pioneering book that delves into the multifaceted dimensions of AI, specifically focusing on OpenAI's revolutionary Chat Generative Pre-Trained Transformer (Chat GPT) and its profound influence on the landscape of communication education. This book navigates the intersection of technology, education, and ethics, shedding light on the imperative need for a collaborative approach to shape AI's evolution. AI's potential to reshape industries and human roles is undeniable. Rooted in the intricate workings of AI and its hallmark, Chat GPT, this book meticulously dissects the dynamic relationship between humans and machines. The discourse extends beyond technology and into the realm of education, asserting that the power to mold AI's trajectory cannot rest solely in the hands of developers. While revealing AI's transformative potential in the communication classroom, the book conscientiously explores ethical concerns and biases, fostering a balanced approach to its integration. This book is instrumental to the ongoing discourse on AI's role in education. The call for ethical considerations, inclusivity, and regulation serves as a guiding compass for educators, students, developers, and policymakers alike. The book ensures a holistic perspective on AI's integration by addressing privacy, citation, voice ownership, and overall digital ethics.

Author : StoryBuddiesPlay
Publisher : StoryBuddiesPlay
Page : 92 pages
File Size : 49,5 Mb
Release : 2024-05-14
Category : Computers
ISBN : 8210379456XXX

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Ollama 3 User Guide by StoryBuddiesPlay Pdf

Unleash the potential of large language models (LLMs) with the comprehensive Ollama 3 User Guide! This in-depth guide equips you with everything you need to master Ollama 3, the user-friendly framework that unlocks the power of LLMs for creative exploration, enhanced productivity, and innovative problem-solving. Get started effortlessly: Learn how to install and configure Ollama 3, navigate the interface with ease, and explore the functionalities that empower you to interact with various LLM models. Craft effective prompts: Discover the art of crafting clear and concise prompts that guide the LLM towards generating the desired outputs, whether it's creative text formats, informative summaries, or code snippets tailored to your needs. Boost your workflow: Ollama 3 goes beyond basic LLM interaction. Explore advanced techniques like multi-step prompts, temperature control to fine-tune creativity, and even fine-tuning models for specific domains, taking your projects to the next level. Navigate the LLM landscape: Delve into the world of LLMs and understand how to interpret their outputs effectively. Learn to assess the context, relevance, accuracy, and potential biases to ensure the LLM's responses align with your goals. Embrace responsible use: As a responsible LLM user, this guide equips you with the knowledge to address potential biases and ethical considerations. Use LLMs for positive purposes and contribute to a future where these powerful tools empower creativity and progress. More than just a user guide: This comprehensive resource extends beyond basic instructions. Explore practical applications of Ollama 3 across various fields, from content creation and code assistance to data analysis and educational tools. Discover how to integrate Ollama 3 with your existing workflow for seamless LLM integration. Join the LLM community: The guide empowers you to connect with the vibrant Ollama 3 community and the broader LLM ecosystem. Learn from experienced users, stay updated on the latest advancements, and contribute to the future of this groundbreaking technology.

Author : Mahmud, Saadia
Publisher : IGI Global
Page : 373 pages
File Size : 49,7 Mb
Release : 2024-02-13
Category : Education
ISBN : 9798369302422

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Academic Integrity in the Age of Artificial Intelligence by Mahmud, Saadia Pdf

Powerful generative Artificial Intelligence (AI) has defined and transformed our modern era, and the fundamental conceptualization of academia stands at a crossroads. Academic Integrity in the Age of Artificial Intelligence delves deep into the seismic shifts and intricate challenges brought forth by the proliferation of AI technologies, exploring the intricacies between innovation and integrity. The rise of generative AI, exemplified by ChatGPT, has set a cascade of change across diverse sectors, including higher education, medicine, and the arts. This book dissects the multifaceted impact of AI on the academic landscape. With AI's ability to craft text and imagery mirroring human creativity, the lines between authentic scholarship and synthetic deception blur. The book examines the delicate balance between productivity and ethics while weaving a comprehensive tapestry of insights from various stakeholders. From academics grappling with the definition of AI-assisted breaches of academic integrity to policymakers reshaping the future of higher education, this book engages a myriad of voices. It scrutinizes the nascent challenges in assessment design, the urgent need to update antiquated integrity policies, and the importance of research ethics in an AI-driven world. This book is ideal for educators, policymakers, students, and technologists through the complicated terrain of AI ethics.